AI-Generated Drug Innovations and Indian Patent Law: Legal Challenges and Ethical Responses

Key Applications of AI in Drug Discovery

• Molecular Generation: AI can generate molecules with specific properties, reducing the time and cost associated with traditional drug discovery methods (Shimizu et al., 2023).
• Patent Status Verification: AI can assist in verifying the patent status of generated compounds, ensuring that new drugs are novel and non-obvious (Shimizu et al., 2023) (Chun, n.d.).
• Personalized Medicine: AI facilitates the development of personalized therapies by analyzing patient-specific data and tailoring treatments to individual needs (Poddar & Rao, 2024) (Kimta & Dogra, 2024).

Intellectual Property Challenges in AI-Generated Pharmaceuticals

Patentability of AI-Generated Inventions

Inventorship in AI-Generated Inventions

Data Exclusivity and AI-Generated Pharmaceuticals

The Way Forward: Regulatory and Policy Recommendations

• Indian patent law should provide clear guidelines for assessing the novelty, non-obviousness, and technical effect of AI-generated pharmaceutical compositions (“Artificial Intelligence and Its Patentability: A Comparative Study Between India,UK, and USA,” 2023) (Bhardwaj & Gupta, n.d.).
• The patent office should establish specific criteria for evaluating the patentability of AI-generated inventions, ensuring consistency and predictability in the patent granting process (“Artificial Intelligence and Its Patentability: A Comparative Study Between India,UK, and USA,” 2023) (Bhardwaj & Gupta, n.d.).

• India should consider revising its patent law to address the question of AI inventorship. This could involve recognizing AI systems as co-inventors or establishing alternative ownership frameworks for AI-generated inventions (“Can Artificial Intelligence (AI) Machine Be Granted Inventorship in India?,” 2023) (Artificial Intelligence and Inventorship Under the Patent Law Regime: Practical Development from Common Law Jurisdictions, 2023).
• International collaboration and harmonization of inventorship rules can provide valuable insights for Indian policymakers (Poddar & Rao, 2024) (Bharati, 2024).

• New data exclusivity frameworks should be developed to accommodate AI-generated pharmaceuticals, ensuring that innovation is incentivized while maintaining access to essential medicines (Kimball & Ragavan, 2022) (Chun, n.d.).
• Policymakers should engage with stakeholders, including pharmaceutical companies, researchers, and patient advocacy groups, to develop balanced and equitable data exclusivity rules (Sabet et al., 2024) (Kimta & Dogra, 2024).

• The Indian government should establish regulatory sandboxes to test and refine IP frameworks for AI-generated pharmaceuticals. These sandboxes can provide a safe environment for innovation while minimizing regulatory risks (Poddar & Rao, 2024) (Bharati, 2024).

• Collaborative efforts between the public and private sectors can accelerate the development of AI-driven pharmaceutical innovations. Such partnerships can also facilitate the creation of standardized datasets and tools for AI-based drug discovery (Kimta & Dogra, 2024) (Sharma, 2020).

Table. Key Aspects of AI in Pharmaceutical Drug Discovery and IP Laws in India.

AI in Pharma Research

AI in Drug Discovery

• AI accelerates drug discovery by identifying potential drug candidates and predicting their efficacy, significantly reducing the time and cost associated with traditional methods (Nihalani, 2024) (Sonia, 2022).
• Machine learning models are used for target identification and validation, virtual screening, and drug repurposing, which streamline the discovery process (Nihalani, 2024) (Acharjee et al., 2023).
• AI-driven algorithms can predict molecular properties and interactions, aiding in the selection of promising compounds for further development (Acharjee et al., 2023) (Amin et al., 2024).

Personalized Medicine

• AI leverages patient-specific data, such as genetic information and clinical histories, to develop personalized treatment plans, enhancing therapeutic effectiveness and minimizing side effects (Nihalani, 2024) (Sonia, 2022).
• Advanced AI models analyze complex biological data to tailor treatments to individual patient profiles, thus improving patient outcomes and reducing adverse reactions (Shah & Shah, 2023) (Halagali et al., 2025).
• AI's role in personalized medicine extends to predicting drug interactions and optimizing treatment regimens based on individual patient characteristics (Serrano et al., 2024) (Suriyaamporn et al., 2024).

Clinical Trials and Drug Safety

• AI optimizes clinical trial design by selecting suitable candidates, managing data, and predicting trial outcomes with greater accuracy, thereby increasing the efficiency of trials (Sonia, 2022) (Mahajan et al., 2024).
• In pharmacovigilance, AI enhances drug safety by enabling real-time analysis of adverse drug reactions and risk assessment, ensuring patient safety post-market (Amin et al., 2024) (Suriyaamporn et al., 2024).

Challenges and Considerations

• Despite its potential, AI in pharmaceuticals faces challenges such as data privacy concerns, ethical considerations, and the need for transparent and interpretable models (Nihalani, 2024) (Sonia, 2022).
• Regulatory frameworks must evolve to accommodate AI technologies, ensuring they are integrated into existing workflows without compromising safety or efficacy (Serrano et al., 2024) (Barua et al., 2024).
• The need for large, high-quality datasets and the high costs associated with AI implementation are additional barriers to widespread adoption (Mahajan et al., 2024).

IP Laws and Pharma Innovation

Incentives for Innovation

• Patents as Incentives: Patents are a primary form of IP protection in the pharmaceutical industry, granting exclusive rights to inventors for a limited period. This exclusivity is crucial for encouraging investment in R&D, as it allows companies to potentially recoup the high costs associated with developing new drugs, which often involve lengthy and expensive clinical trials (Cockburn & Long, 2015) (Gawel, 2016).
• Economic Growth and Investment: IP laws, particularly patents, are vital for attracting private investment in high-risk biomedical research. Without these protections, private investment would likely diminish, negatively impacting public funding for fundamental research and slowing down innovation in neglected fields such as rare diseases (Wise, 2014).
• Facilitating Access to Capital: A robust patent system is essential for enabling access to capital markets, which is necessary for funding the development of innovative pharmaceutical products. This is particularly important in regions with restricted access to investors (Gawel, 2016).

Challenges and Controversies

• Access to Medicines: While patents incentivize innovation, they can also create monopolies that lead to high drug prices, limiting access to essential medicines, especially in developing countries. This tension between patent protection and public health objectives is a significant challenge, as seen in the case of HIV/AIDS treatment, where generic competition drastically reduced prices (Said & Kapczynski, 2012) (Rehan et al., 2024).
• Balancing Innovation and Public Health: The monopolistic nature of patents can hinder access to medicines, necessitating a balance between fostering innovation and ensuring affordable access. Strategies such as compulsory licensing and parallel imports are employed to address these issues, aiming to reconcile patent rights with public health priorities (Rehan et al., 2024).

Legal and Policy Considerations

• Regulatory Frameworks: The legal protection of pharmaceuticals involves compliance with national and international IP laws and treaties. These frameworks are designed to protect pharmaceutical research results and ensure fair distribution of benefits from medical innovations (Lei, n.d.).
• Impact of IP Policies: The shaping and implementation of IP policies significantly influence the rate and direction of pharmaceutical innovation. Strong patent laws can undermine access to medicines in developing countries, highlighting the need for policies that balance innovation incentives with access to healthcare (Said & Kapczynski, 2012).

AI in India's Pharma Revolution

AI in Drug Discovery and Development

• AI accelerates drug discovery by expediting target identification, virtual screening, and drug repurposing, significantly reducing the time and cost involved in these processes (Nihalani, 2024).
• Machine learning and deep learning algorithms enhance the identification and optimization of drug candidates, predict clinical trial outcomes, and personalize patient treatment plans, thereby increasing the accuracy and efficiency of drug development (Vij, 2024).
• AI-driven techniques facilitate the simulation of physiological conditions and prediction of drug interactions, optimizing research processes and reducing production costs (Halagali et al., 2025).

Manufacturing and Quality Control

• AI technologies improve manufacturing efficiency and ensure stringent quality control measures, revolutionizing post-market surveillance methodologies (Huanbutta et al., 2024).
• Robotic automation and AI-driven data analytics enhance drug formulation and manufacturing processes, improving pharmacovigilance and drug safety (Nihalani, 2024).
• AI's role in optimizing formulation processes and predicting pharmacokinetics profiles facilitates a more efficient pathway from pilot study to market (Halagali et al., 2025).

Personalized Medicine and Patient Care

• AI enables precision medicine by tailoring treatments to individual patient profiles, genetics, and environmental factors, maximizing treatment efficacy and minimizing adverse reactions (Das et al., 2023).
• AI-powered applications track and monitor patients' medication schedules, reducing adverse reactions and enhancing treatment effectiveness (Das et al., 2023).
• Personalized drug prescriptions and dosage recommendations minimize medication errors, offering a more patient-centered approach to healthcare (Vij, 2024).

Challenges and Strategic Collaboration

• The integration of AI in the pharmaceutical sector faces challenges such as financial limitations, workforce competency, data security concerns, and regulatory complexities (Kimta & Dogra, 2024).
• Successful AI integration requires collaborative efforts from the public and private sectors, emphasizing resource allocation for education, infrastructure, regulatory reforms, and workforce development (Kimta & Dogra, 2024).
• Addressing ethical considerations and data privacy concerns is crucial for the responsible and effective utilization of AI in the pharmaceutical industry (Nihalani, 2024).

AI Pharma Patents Under Indian IP Law

Challenges in Applying Indian IP Laws to AI-Generated Works

• Lack of Legal Recognition for AI as Inventor: Indian patent law, like many others, requires a human inventor for patent applications. This presents a challenge for AI-generated pharmaceutical compositions, as AI cannot be recognized as an inventor under current laws (Vaish et al., 2023) (Ogwuche, 2023).
• Ownership and Authorship Issues: The question of who owns the rights to AI-generated inventions is unresolved. Traditional IP laws focus on human creativity, leaving a gap in protection for works generated autonomously by AI (Munshi & Barai, 2022) (Ahmed, 2025).
• Patentability Concerns: AI-generated pharmaceutical compositions may face hurdles in meeting the criteria for patentability, such as novelty and non-obviousness, especially if the AI's processes are not transparent or easily understood (Poddar & Rao, 2024) (Bharati, 2024).

Opportunities for Legal Reform

• Policy Development: There is a need for policy solutions that address the unique challenges posed by AI in the pharmaceutical sector. This includes considering AI as a tool rather than an inventor, and potentially recognizing the developers or users of AI as the rightful owners of AI-generated inventions (AUTHOR_ID, 2024) (Makam, 2023).
• International Comparisons: Examining how other jurisdictions, such as the US, EU, and China, are addressing AI-generated IP can provide valuable insights for India. These regions are exploring various approaches to integrate AI into their IP frameworks, which could inform India's policy development (Poddar & Rao, 2024) (Ahmed, 2025).
• Collaborative Approach: Engaging stakeholders from various sectors, including policymakers, legal experts, and industry players, can help create a stable IP regime that supports innovation while protecting AI-generated works (Poddar & Rao, 2024) (Rusinovich, 2023).

AI and IP Law Challenges

Legal and Ethical Challenges

• Authorship and Ownership: Traditional IP laws require human authorship and inventorship, which poses a challenge for AI-generated works. This requirement limits the legal recognition of fully autonomous AI-generated content, creating ambiguity in ownership rights (Oğul, 2024) (Marchenko et al., 2024).
• Copyright Infringement and Fair Use: AI's ability to generate content similar to existing works raises concerns about copyright infringement. Determining what constitutes fair use in the context of AI-generated content is complex and requires careful consideration of existing legal standards (Oğul, 2024) (Rusinovich, 2023).
• Ethical Concerns: The ethical implications of AI in IP include issues such as privacy breaches, the propagation of deepfakes, and the potential for AI to perpetuate biases. These concerns highlight the need for ethical guidelines and regulations to ensure responsible AI use (Sharma & Sharma, 2024) (Singh, 2024).
• Liability and Accountability: Determining liability for AI-generated content is challenging, as traditional legal frameworks do not account for the autonomous nature of AI. This raises questions about accountability and the need for new legal standards to address these issues (Singh, 2024) (Mahingoda, 2024).

Proposed Reforms

• New Categories for AI-Generated Works: Legal frameworks should consider creating new categories for AI-generated works to address the unique characteristics of these creations. This could involve recognizing AI as a co-author or inventor, thereby providing clarity on ownership rights (Marchenko et al., 2024) (Mahingoda, 2024). The criminalization of copyright infringement and persistent judicial backlog in India pose significant challenges to effective IP enforcement (Rahul & Yadav, 2025). The protection of moral rights under Indian copyright law, which ensures creators’ attribution and integrity of their work, could inform ownership frameworks for AI-generated pharmaceutical compositions, addressing gaps in recognizing non-human contributions (Yadav, Yadav, Singh, Rajpurohit, & Singh, 2025).
• Harmonization of IP Laws: There is a need for greater harmonization of IP laws across jurisdictions to address the global nature of AI technologies. This includes aligning national regulations with international agreements and ensuring consistency in the application of IP laws (Marchenko et al., 2024) (Huda et al., 2024).
• AI-Powered Enforcement Tools: Investing in AI-driven tools for monitoring and enforcing IP rights can enhance the effectiveness of IP protection. These tools can help identify potential infringements and streamline enforcement processes (Marchenko et al., 2024).
• Ethical Guidelines and Global Standards: Developing ethical guidelines and global standards for AI use can help address ethical concerns and ensure responsible AI deployment. This includes establishing principles for transparency, accountability, and fairness in AI applications (Sharma & Sharma, 2024) (Rotenberg, 2023).

TRIPS Impact on Indian Patent Law

Amendments to the Indian Patents Act

• The Indian Patents Act, 1970, was amended to comply with the TRIPS Agreement, introducing product patents in pharmaceuticals after a 35-year gap. This was a significant shift from the earlier process patent regime, which allowed Indian companies to produce generic versions of patented drugs using alternative processes (Nair, 2008) (Vijayaraghavan & Raghuvanshi, 2008).
• Key amendments occurred in 1999, 2002, and 2005, each aimed at aligning Indian patent law with TRIPS requirements. These amendments extended the patent term to 20 years and introduced provisions for product patents, which were previously not allowed in India (Mathur, 2012) (Nair, 2008).

Impact on the Pharmaceutical Industry

• The introduction of product patents has led to concerns about increased drug prices and restricted access to medicines. However, the Indian government has implemented TRIPS flexibilities, such as compulsory licensing and Section 3(d), to mitigate these effects (Arora & Chaturvedi, 2017) (Pai, 2015).
• Section 3(d) of the Indian Patents Act restricts patents on new forms of known substances unless they demonstrate significant efficacy, a provision that has been pivotal in cases like Novartis v. Union of India (Pai, 2015) (Ali, 2015).
• Compulsory licensing, as seen in the case of Bayer's drug Nexavar, allows for the production of generic versions of patented drugs under certain conditions, ensuring access to essential medicines (Pai, 2015).

Balancing Innovation and Access

• The Indian patent regime aims to balance the rights of patent holders with public health needs. This balance is crucial for maintaining India's status as a major supplier of affordable generic medicines globally (Nair & Fernandes, 2014) (Arora & Chaturvedi, 2017).
• The Drug Price Control Order (DPCO) and the National List of Essential Medicines (NLEM) are additional measures to control drug prices and ensure access to essential medicines (Arora & Chaturvedi, 2017).

Challenges and Criticisms

• Despite the amendments, there are criticisms regarding the narrow focus of patent reforms and the domestic factors influencing these changes. The shared consensus on patent reform has been seen as limiting broader discussions on intellectual property rights (Rangnekar, 2005) (Rangnekar, 2006).
• The Indian pharmaceutical industry has had to adapt to the new patent regime, which has led to increased investment in R&D and a shift in business strategies to comply with international standards (Alam & Rastogi, 2016).

TRIPS and Indian Pharma Patents

Amendments to the Indian Patents Act

• The Indian Patents Act, 1970, was amended to comply with the TRIPS Agreement, introducing product patents in pharmaceuticals after a 35-year gap (In 2025, 55-year gap). This was a significant shift from the earlier process patent regime, which allowed Indian companies to produce generic versions of patented drugs using alternative processes (Nair, 2008) (Vijayaraghavan & Raghuvanshi, 2008).
• Key amendments occurred in 1999, 2002, and 2005, each aimed at aligning Indian patent law with TRIPS requirements. These amendments extended the patent term to 20 years and introduced provisions for product patents, which were previously not allowed in India (Mathur, 2012) (Nair, 2008).

Impact on the Pharmaceutical Industry

• The introduction of product patents has led to concerns about increased drug prices and restricted access to medicines. However, the Indian government has implemented TRIPS flexibilities, such as compulsory licensing and Section 3(d), to mitigate these effects (Arora & Chaturvedi, 2017) (Pai, 2015).
• Section 3(d) of the Indian Patents Act restricts patents on new forms of known substances unless they demonstrate significant efficacy, a provision that has been pivotal in cases like Novartis v. Union of India (Pai, 2015) (Ali, 2015).
• Compulsory licensing, as seen in the case of Bayer's drug Nexavar, allows for the production of generic versions of patented drugs under certain conditions, ensuring access to essential medicines (Pai, 2015).

Balancing Innovation and Access

• The Indian patent regime aims to balance the rights of patent holders with public health needs. This balance is crucial for maintaining India's status as a major supplier of affordable generic medicines globally (Nair & Fernandes, 2014) (Arora & Chaturvedi, 2017).
• The Drug Price Control Order (DPCO) and the National List of Essential Medicines (NLEM) are additional measures to control drug prices and ensure access to essential medicines (Arora & Chaturvedi, 2017).

Challenges and Criticisms

• Despite the amendments, there are criticisms regarding the narrow focus of patent reforms and the domestic factors influencing these changes. The shared consensus on patent reform has been seen as limiting broader discussions on intellectual property rights (Rangnekar, 2005) (Rangnekar, 2006).
• The Indian pharmaceutical industry has had to adapt to the new patent regime, which has led to increased investment in R&D and a shift in business strategies to comply with international standards (Alam & Rastogi, 2016).

AI in Drug Discovery and Formulation

AI Algorithms in Molecular Design

• Target Identification and Lead Optimization: AI algorithms, particularly machine learning models, are instrumental in identifying novel drug targets by analyzing high-dimensional omics data. These models predict molecular properties and pharmacological profiles, which are crucial for lead optimization (Ozaybi et al., 2024) (Mukherjee et al., 2024).
• Structure-Based Drug Design: AI-driven methodologies, such as deep learning, facilitate the design and synthesis of novel drug candidates with enhanced efficacy and specificity. These algorithms explore vast chemical spaces, enabling the discovery of compounds with improved therapeutic potential (Afrose et al., 2024).
• Predictive Modeling: AI models predict important aspects of potential drug candidates, including their pharmacokinetic properties and possible toxicity. This predictive capability streamlines the drug discovery process by reducing risks associated with further development (Mukherjee et al., 2024).

AI in Formulation and Drug Delivery

• Optimization of Drug Formulations: AI and machine learning techniques are increasingly used to optimize drug delivery systems. They are effective in devising controlled-release formulations and nano-scale delivery platforms, enhancing the durability and efficiency of drug carriers (Dey et al., 2024).
• Process Parameter Optimization: AI plays a pivotal role in optimizing process parameters during drug manufacturing. Algorithms enable real-time monitoring and control, ensuring the quality and consistency of drug products. This is achieved through iterative learning and adaptive control, which dynamically optimize manufacturing parameters (Pandey et al., 2024).
• Personalized Medicine: AI facilitates the development of personalized treatment plans by integrating multi-omics data sets and predicting drug-target interactions. This approach enhances therapeutic efficacy and reduces side effects by tailoring treatments to individual patient profiles (Afrose et al., 2024).
• Challenges and Considerations: While AI offers transformative potential in drug discovery and formulation, several challenges persist. Data quality and scarcity, regulatory complexities, and ethical considerations are significant hurdles that need to be addressed. Moreover, the interpretability of AI models and the need for standardized frameworks are critical for the successful integration of AI in pharmaceutical research (Dey et al., 2024) (Abbas et al., 2024) (Ozaybi et al., 2024). Despite these challenges, ongoing advancements in AI technologies and interdisciplinary collaborations hold promise for overcoming these obstacles and fully leveraging AI's capabilities in drug discovery (Ozaybi et al., 2024).

AI-Generated Drug Compositions

Generative Drug Discovery

• Generative AI has enabled the de novo design of drug-like compounds, with several AI-designed molecules entering preclinical and clinical trials. Techniques such as deep learning and novel architectures like Transformers have been pivotal in creating compounds with desirable drug-like properties (Vanhaelen et al., 2024).
• A notable case study involves the use of AI to generate novel molecules that activate the μ-opioid receptor and penetrate the blood-brain barrier. Out of 180,000 generated structures, 78% were chemically valid, and 31% met the desired property criteria, showcasing the potential of AI in creating novel therapeutic agents (Wang et al., 2021).

In Silico Formulation Optimization

• AI has been applied to optimize pharmaceutical formulations, reducing reliance on traditional experimental methods. For instance, a generative AI method was used to digitally create and optimize drug formulations, such as determining the percolation threshold in oral tablets and optimizing drug distribution in HIV inhibitor implants. These AI-generated formulations demonstrated comparable properties to real samples, validating the method's accuracy (Hornick et al., 2024).
• AI-driven formulation design has also been employed to predict drug-excipient compatibility and optimize drug delivery systems, enhancing the efficiency and success rates of pharmaceutical development (Saha, 2024) (Dey et al., 2024).

Patent-Aware Molecule Generation

• AI has been utilized to generate novel pharmaceutical compounds while considering patent status. By incorporating patent information into the learning process, AI systems can produce drug-like molecules that are both novel and non-infringing, facilitating efficient drug development (Shimizu et al., 2023).

AI in Drug Safety and Pharmacovigilance

• Generative AI plays a crucial role in pharmacovigilance by enhancing adverse event detection and risk prediction. This application of AI ensures the safety of both new and existing pharmaceuticals, streamlining safety assessments and improving patient outcomes (Mishra & Gupta, 2024).

AI in Drug Discovery

Accelerating Drug Discovery

• Predictive Modeling: AI models, such as the AI-DTI, are used to predict drug-target interactions, which are crucial for understanding mechanisms of action and identifying potential drug candidates. These models have been successfully applied in rediscovering treatments for diseases like COVID-19 (Bindu et al., 2024).
• Generative AI: This technology is used to design novel molecular structures and simulate biological interactions, significantly speeding up the early stages of drug discovery (Gatla, 2020).
• Bioactivity and Physicochemical Forecasting: AI systems like AI-D3S utilize advanced algorithms to predict bioactivity and physicochemical properties, which are essential for identifying promising drug candidates (“Advances of AI-Driven Drug Design and Discovery in Pharmaceuticals - Review,” 2024).

Reducing Costs

• Optimization of Clinical Trials: AI enhances the efficiency of clinical trials by predicting failures, personalizing treatments, and optimizing patient recruitment, which reduces the overall cost of drug development (Pereira, 2024).
• Predictive Toxicology: By forecasting potential toxicities and side effects, AI reduces late-stage failures, thereby saving costs associated with unsuccessful drug candidates (Patnaik et al., 2023).
• Streamlining Processes: AI simplifies labor-intensive processes by analyzing and sorting large data volumes, which helps in identifying the most promising drug candidates more quickly and cost-effectively (Krishnababu et al., 2023).

Enhancing Precision and Personalization

• Target Identification and Lead Optimization: AI facilitates the identification of drug targets and the optimization of lead compounds, which are critical steps in developing effective therapies (Yadav et al., 2024).
• Drug Repurposing: AI aids in finding new therapeutic applications for existing drugs, which can be a cost-effective strategy for drug development (Patnaik et al., 2023).
• Personalized Medicine: AI's ability to analyze vast datasets allows for the development of personalized therapies, improving patient outcomes and reducing the risk of adverse effects (Ujjwal, 2024).

Ethics of AI in Pharma and Healthcare

Bias in AI Algorithms

• AI algorithms in healthcare and pharmaceuticals can reflect existing biases present in the training data, leading to inequitable healthcare outcomes. This is a significant concern as biased algorithms can perpetuate disparities in healthcare delivery and drug development (Dara & Azarpira, 2025) (Agrawal, 2024).
• To mitigate bias, it is essential to use diverse and representative datasets for training AI models. This approach helps ensure that AI systems provide fair and accurate results across different demographic groups (Bergemann et al., 2024).
• Bias testing and the development of explainable AI (XAI) are crucial strategies to identify and address biases in AI systems, promoting transparency and trust in AI-driven decision-making processes (Jeyaraman et al., 2023) (Bergemann et al., 2024).

Transparency and Reproducibility of AI-Generated Results

• Transparency in AI decision-making is vital to build trust among stakeholders, including healthcare professionals, patients, and regulatory bodies. The "black box" nature of many AI models poses challenges to understanding and validating AI-generated results (Dara & Azarpira, 2025) (Agrawal, 2024).
• Explainable AI (XAI) techniques are essential to provide insights into how AI models arrive at specific conclusions, thereby enhancing the reproducibility and reliability of AI-generated outcomes (Jeyaraman et al., 2023).
• Regulatory frameworks and guidelines are necessary to ensure that AI systems in healthcare and pharmaceuticals operate transparently and adhere to ethical standards, facilitating the reproducibility of results across different settings (Kang, 2024) (Pitel et al., 2024).

Public Health Considerations in India

• AI has the potential to revolutionize healthcare in India by enhancing diagnosis, treatment, and healthcare systems. However, ethical considerations such as data privacy, transparency, and bias mitigation are crucial to ensure equitable access to AI-driven healthcare solutions (“AI in Healthcare: Enhancing Diagnosis, Treatment, and Healthcare Systems for a Smarter Future in India,” 2023) (Neyigapula, 2023).
• The implementation of AI in Indian healthcare systems requires robust regulatory frameworks to address ethical challenges and promote responsible innovation. This includes ensuring data privacy and security, particularly when handling sensitive health information (Kumar & Velusamy, 2025) (Singh et al., 2023).
• Public health strategies should focus on promoting diversity in AI research and the workforce to address healthcare inequalities and ensure that AI technologies benefit all segments of the population (Jeyaraman et al., 2023).

TRIPS Amendment & Access to Medicines

TRIPS Amendment and Compulsory Licensing

• The 2005 TRIPS amendment was designed to address the needs of countries with insufficient pharmaceutical manufacturing capabilities by allowing them to import generic drugs produced under compulsory licenses (Herget, 2006).
• Despite this, the amendment's implementation has been slow, with fewer WTO members accepting the protocol than required for it to take full effect (Kennedy, 2010).
• Compulsory licensing has been a critical tool for countries like India, enabling them to produce affordable generic medicines and address public health needs during emergencies (Shanmugaiah, 2012).

Impact on Indian Pharmaceutical Industry

• India's transition to a TRIPS-compliant patent regime has been marked by strategic use of TRIPS flexibilities, such as compulsory licensing and the Bolar provision, to support its generic drug industry (Nair et al., 2012) (Shanmugaiah, 2012).
• The Indian pharmaceutical sector has seen increased R&D investment and a shift towards regulated markets, with a significant number of USFDA and EMA/EDQM approved manufacturing sites (Nair et al., 2012).
• The 2005 Patents (Amendment) Act introduced safeguards against non-meritorious patents, although debates continue over the scope of patentability, particularly concerning New Chemical Entities (NCEs) (Basheer, n.d.).

Challenges and Global Perspectives

• The TRIPS Agreement has been criticized for creating barriers to affordable medicines in developing countries, as it strengthens patent protections that can limit access to essential drugs (Verma, 2011).
• Pharmaceutical multinationals have been resistant to changes that would facilitate the production of generics, impacting technology transfer and access to medicines in poorer countries (Vicente, 2010).
• The debate over whether TRIPS should exclude pharmaceutical patents from extended IP rights protection continues, highlighting the tension between global IP rights and public health needs (Sundaram, 2023).

AI as Inventor? Legal & Ethical Debate

Legal Challenges

• Human-Centric Patent Laws: Most jurisdictions, including the European Union, maintain that only humans can be inventors, which excludes AI from being recognized as inventors under current patent laws. This is based on the premise that AI lacks legal personality and the human-centric approach to inventorship (Buzu, 2024) (Matulionytė, 2024).
• Case Law and Precedents: The DABUS case and the UK Supreme Court decision in Thaler v Comptroller of Patents have reinforced the stance that AI cannot be an inventor, highlighting the gaps in patent law that need addressing as AI technologies evolve (Buzu, 2024) (Matulionytė, 2024).
• Patentability and Inventorship Criteria: The current legal framework struggles to accommodate AI-generated inventions, as traditional inventorship criteria emphasize human creativity and conception, which AI lacks (Vaish et al., 2023) (Lamlert, 2020).

Ethical and Social Considerations

• Incentivizing Human Creativity: Recognizing AI as inventors could potentially diminish the role of human ingenuity in the inventive process, raising concerns about the future of human creativity and innovation (Matulionytė, 2024).
• Intellectual Property Rights: Granting inventorship to AI could lead to broader implications for intellectual property rights, including issues of ownership and liability, which are not adequately addressed by existing laws (Bisoyi, 2022) (Ogwuche, 2023).
• Spectrum of AI Contribution: The debate also considers the spectrum of AI involvement in the invention process, from AI as a tool to AI as a primary inventor, which complicates the attribution of inventorship (Nietering, 2022).

Technological Considerations

• Autonomy and Inventiveness: The technological capability of AI to autonomously generate inventions challenges the traditional notion of inventorship, as AI systems can perform the conception step, which is central to inventorship (Geigel, 2022).
• Machine Learning and Inventive Processes: The role of machine learning in AI's inventive capabilities raises questions about the criteria for evaluating AI's contribution to inventions and the level of autonomy required for inventorship claims (Geigel, 2022).

AI, Trade Secrets & Trademarks in Pharma

Trade Secrets in AI Pharmaceutical Formulations

• AI-Generated Information: AI systems in pharmaceutical formulations generate valuable data, such as optimal excipient combinations and stability profiles, which can qualify as trade secrets. The protection of such AI-generated information is crucial, as it represents a competitive advantage for companies (Sprankling, 2024) (Saha, 2024).
• Ownership and Protection: Determining the ownership of AI-generated trade secrets poses a challenge. The legal framework must address whether the AI system, its developers, or the employing company holds the rights to these secrets. This is particularly important as AI systems increasingly contribute to the creation of proprietary information (Sprankling, 2024).
• Policy Recalibration: Existing doctrines that limit trade secret protection may need recalibration to accommodate AI's capabilities. This includes considering how AI can potentially terminate certain human-created secrets and how trade secret law can mitigate AI-related risks (Sprankling, 2024).

Trademarks in AI Pharmaceutical Formulations

• Trademark Challenges: The use of AI in pharmaceutical formulations raises questions about trademark protection, especially when AI systems generate new technological achievements. The current legal framework often struggles to cover these innovations, leading to disputes over trademark rights (Cai, 2024).
• Training AI Systems: The use of trademark-protected marks in training AI systems is a contentious issue. The primary function of trademarks is to distinguish products in commerce, and this function may be disrupted during AI training. Legal frameworks need to define the boundaries of trademark protection in this context (Ninković, 2024).
• Trademark Infringement: The identification of trademark rights for AI-generated achievements requires improvements in legal provisions. Introducing standards like "confusion possibility" can help determine trademark infringement in AI contexts (Cai, 2024).

AI as Inventor Under Indian Law

Current Legal Framework in India

• The Indian Patents Act mandates that an inventor must be a natural person, which inherently excludes AI systems from being recognized as inventors (“Can Artificial Intelligence (AI) Machine Be Granted Inventorship in India?,” 2023) (Devarapalli, 2019).
• The Supreme Court of India has previously recognized non-human entities as juridical persons in certain contexts, which could potentially be extended to AI systems, but this has not yet been applied to patent law (Ganguli, 2024).

Global Perspectives and Precedents

• Globally, there is no consensus on AI inventorship. Countries like the USA, UK, and EU have rejected AI as inventors, while Australia and South Africa have recognized AI in this role (“Can Artificial Intelligence (AI) Machine Be Granted Inventorship in India?,” 2023) (Mutale, 2024).
• The DABUS case, where an AI was named as an inventor, has been pivotal in sparking international debate. The case highlighted the challenges of recognizing AI as inventors due to the lack of legal personality and the human-centric nature of current patent laws (Matulionytė, 2024) (Engel, 2020).

Challenges and Considerations

• Recognizing AI as inventors raises questions about ownership, liability, and the fundamental nature of inventorship, which traditionally involves human creativity and decision-making (Bisoyi, 2022) (Artificial Intelligence and Inventorship Under the Patent Law Regime: Practical Development from Common Law Jurisdictions, 2023).
• There is a need for a sui generis system or a new legal framework to address the unique challenges posed by AI-generated inventions, ensuring fair treatment and protection of such innovations (Mutale, 2024) (Bisoyi, 2022).

Potential for Future Developments

• The recognition of AI as inventors could encourage innovation and the development of AI technologies, but it also risks diminishing human involvement in the inventive process (Matulionytė, 2024).
• A collaborative approach involving policymakers, legal experts, and industry stakeholders is essential to navigate the evolving intellectual property landscape and to potentially harmonize international standards (Poddar & Rao, 2024).

Global Patent Office Views on AI Inventions

USPTO Perspective

• The United States Patent and Trademark Office (USPTO) has taken a firm stance that AI cannot be listed as an inventor. This decision is based on statutory language that requires an inventor to be a natural person. However, the USPTO acknowledges that AI can assist in the inventive process, provided a human makes a substantial contribution to the conception or reduction to practice of the invention (Engel, 2020) (Hou, 2024).
• The USPTO is also exploring the impact of AI on prior art and the "person having ordinary skill in the art" (PHOSITA) standard, which could affect patentability assessments, particularly in the pharmaceutical sector where AI is increasingly used for drug discovery (Hou, 2024).

EPO Perspective

• The European Patent Office (EPO) has focused on formal rules, requiring that a human be named as the inventor, even if AI played a significant role in the invention process. This approach is consistent with the EPO's requirement for detailed disclosure of machine learning algorithms and the characteristics of training data, which are crucial for assessing the technical effect of an AI algorithm (Engel, 2020) (Hou, 2024).
• The EPO's stance reflects a broader European approach that emphasizes the need for transparency and detailed technical disclosure in AI-related patent applications, which is particularly relevant in the pharmaceutical industry where AI is used to optimize drug formulations and manufacturing processes (Huanbutta et al., 2024).

UKIPO Perspective

• The UK Intellectual Property Office (UKIPO) has considered more substantive aspects of AI inventorship, focusing on the contribution of AI to the inventive process. Like the USPTO and EPO, the UKIPO requires a human to be named as the inventor, reflecting a lack of consensus on recognizing AI as an inventor (Engel, 2020).
• The UKIPO's approach is part of a broader legal landscape in the UK that is adapting to the challenges posed by AI in intellectual property, including the pharmaceutical sector where AI is used for drug discovery and development (Rusinovich, 2023) (Rosati, 2024).

Broader Implications and Challenges

• The rulings from these patent offices highlight the ongoing challenges in harmonizing international patent laws to accommodate AI-generated inventions. The lack of a universal approach to AI inventorship and patentability could lead to inconsistencies and legal uncertainties, particularly in the pharmaceutical industry where AI is driving significant innovation (Bryukhovetsky, n.d.) (Poddar & Rao, 2024).
• There is a growing recognition of the need for regulatory cooperation and harmonization to address these challenges, ensuring that AI-driven innovations can be effectively protected and commercialized across different jurisdictions (Khan, 2024) (Voitovych et al., 2021).

Section 3(d) and Pharma Patents

Key Provisions of the Indian Patents Act, 1970

• Section 3(d) Overview: This section restricts the patentability of new forms of known substances unless they result in enhanced efficacy. It aims to prevent the extension of patent life through trivial modifications, a practice known as evergreening (Y et al., 2024) (Sohrabji & Maloney, 2020).
• Impact on Pharmaceutical Patents: Section 3(d) has been instrumental in rejecting patents that do not demonstrate significant improvements in efficacy, as seen in the high-profile Novartis Glivec case, where the Supreme Court upheld the rejection of a patent application based on this provision (Sohrabji & Maloney, 2020) (Basheer & Reddy, 2008).
• Compulsory Licensing: The Act includes provisions for compulsory licensing, allowing generic production of patented drugs under certain conditions, which has been crucial for maintaining drug affordability in India (Y et al., 2024).

Patentability Criteria

• Efficacy Requirement: The requirement for demonstrating enhanced efficacy under Section 3(d) has been a contentious issue, with critics arguing that it may stifle genuine innovation by setting a high bar for patentability (Goudra, n.d.) (Basheer & Reddy, 2008).
• Statistical Analysis: Studies have shown that patent applications with Section 3(d) objections are more likely to be rejected, indicating its significant role in the patent examination process (Sohrabji & Maloney, 2020).
• Evolving Use: Over time, the application of Section 3(d) has increased, including its use against primary patent applications, which raises concerns about potential over-utilization and its impact on innovation (Sampat & Shadlen, 2018).

Challenges and Criticisms

• Ambiguity and Interpretation: The lack of a clear definition of "efficacy" has led to divergent interpretations and application inconsistencies, prompting calls for clearer guidelines and potential amendments to the provision (Goudra, n.d.) (Kant, 2013).
• International and Domestic Tensions: The provision reflects broader tensions between national interests in public health and international patent obligations, with debates on whether it aligns with TRIPS standards (Sampat & Shadlen, 2015) (Basheer & Reddy, 2008).

AI Innovation and Section 3(d)

Impact of Section 3(d) on AI-Generated Innovations

• Incremental Innovations: AI technologies often produce incremental innovations by optimizing existing processes or compounds. Section 3(d) poses a challenge as it requires a demonstration of enhanced efficacy for patent eligibility, which may not always be evident in AI-generated improvements (Poddar & Rao, 2024) (Fabris, 2020).
• Patentability Challenges: The inventive step requirement, crucial for patentability, becomes complex with AI-generated inventions. AI can make certain innovations appear obvious, complicating the assessment of non-obviousness, a key criterion for patent eligibility (Ramalho, 2018) (Fabris, 2020).
• Global Perspectives: Different jurisdictions have varied approaches to AI-related patentability. For instance, the U.S. and EU emphasize human involvement in the inventive process, which can limit the patentability of AI-generated innovations (Hou, 2024) (Matulionytė, 2024).

Balancing Innovation with Generic Drug Production

• Generic Drug Production: Section 3(d) supports the production of generic drugs by preventing the extension of patent life through minor modifications. This is crucial for maintaining drug affordability and accessibility (Poddar & Rao, 2024).
• AI in Drug Discovery: AI's role in drug discovery can lead to new formulations or uses of existing drugs. While these innovations can be significant, they must meet the enhanced efficacy requirement to be patentable under Section 3(d), ensuring that only truly novel advancements receive protection (Fabris, 2020) (Kazimi & Thalwal, 2024).
• Policy Implications: Policymakers must balance encouraging AI-driven innovation with the need to keep essential medicines affordable. This involves ensuring that patent laws do not stifle innovation while also preventing the monopolization of minor advancements (Kazimi & Thalwal, 2024) (Whalen & Zingg, 2022).

AI and Patent Law

AI Pharma IP Infringement Challenges

Liability and Legal Frameworks

• Liability Assignment: Determining who is liable for AI-generated patent infringements is a significant challenge. Current legal systems often struggle to assign responsibility, as AI systems operate with a degree of autonomy that complicates traditional liability frameworks. The liability could potentially fall on developers, users, or the AI systems themselves, but existing laws like the Digital Millennium Copyright Act (DMCA) are not equipped to handle such cases (Eviani et al., 2024) (Yang, 2024).
• Patent Law Limitations: The case of Thaler v Comptroller of Patents highlights the limitations of current patent laws, which do not recognize AI as an inventor. This human-centric approach may hinder the development of AI technologies capable of autonomous invention, as it does not accommodate non-human inventors (Matulionytė, 2024).

Technical Challenges in Proving Infringement

• Similarity and Originality: AI-generated compositions may closely resemble existing patented drugs due to the nature of AI training processes, which often involve large datasets of existing works. This raises questions about the originality of AI-generated outputs and whether they constitute independent creations or infringe on existing patents (Yang, 2024) (Demir, 2024).
• Automated Assessment Tools: Tools like PatentFinder have been developed to address the technical challenges of assessing patent infringement in AI-generated compositions. These tools use advanced algorithms to evaluate molecular structures for potential infringement, offering a more systematic approach to identifying and proving infringement (Shi et al., 2024).

Ethical and Policy Considerations

• Ethical Implications: The use of AI in drug discovery raises ethical questions about the balance between innovation and the protection of intellectual property rights. There is a need for legal frameworks that not only address liability but also consider the ethical implications of AI-generated inventions (Shukla, 2024) (Bratajaya, 2024).
• Policy Development: There is a call for new legal frameworks and policies that can better accommodate AI-generated content. This includes considering AI as a potential legal author and exploring alternative licensing arrangements to balance the interests of creators, users, and the public (Shukla, 2024) (Rosati, 2024).

Reforming Indian IP for AI-Generated Pharma

Challenges to Existing IP Frameworks

• Patentability Issues: AI-generated pharmaceutical compositions challenge the traditional criteria for patentability, which include novelty, inventive step, and industrial applicability. The question of inventorship is particularly problematic, as current laws require a human inventor, whereas AI systems can autonomously generate novel compositions without direct human intervention (Munshi & Barai, 2022) (Poddar & Rao, 2024).
• Data Exclusivity: AI's role in drug development also raises questions about data exclusivity, a form of protection that prevents competitors from using clinical trial data to gain market approval for generic versions of drugs. The duration and scope of data exclusivity may need to be reconsidered in light of AI's ability to rapidly generate and analyze vast datasets (Kimball & Ragavan, 2022).
• Copyright Concerns: While copyright law traditionally protects creative works, its application to AI-generated content is ambiguous. In India, as in many jurisdictions, copyright protection is contingent upon human authorship, leaving AI-generated works in a legal gray area (AUTHOR_ID, 2024) (Makam, 2023).

Potential Legal Reforms

• Redefining Inventorship: Legal reforms may be necessary to redefine inventorship to include AI systems or to recognize the contributions of AI as part of a collaborative human-AI inventorship model. This could involve creating new categories of IP rights specifically for AI-generated inventions (Rusinovich, 2023) (Bharati, 2024).
• Policy Solutions for Data Exclusivity: Policymakers might consider revising data exclusivity laws to account for the unique capabilities of AI in drug development. This could involve shorter exclusivity periods or alternative forms of protection that incentivize innovation while ensuring public access to affordable medicines (Kimball & Ragavan, 2022).
• International Harmonization: Given the global nature of pharmaceutical markets, international cooperation and harmonization of IP laws concerning AI-generated works could provide a more consistent and predictable legal environment. This would involve aligning India's IP laws with those of other major jurisdictions, such as the US, EU, and China, which are also grappling with similar challenges (Poddar & Rao, 2024) (Kazimi & Thalwal, 2024).

AI Pharma Patents: Indian IP Challenges & Reforms

Challenges in IP Protection for AI-Generated Pharmaceuticals

• Inventorship and Ownership: Indian IP laws, similar to those in other countries, traditionally require a human inventor for patent applications. This poses a challenge for AI-generated pharmaceutical compositions, as AI systems can independently create novel drug formulations without direct human intervention. The current legal framework does not recognize AI as an inventor, leading to potential disputes over ownership and rights to AI-generated inventions (Ogwuche, 2023) (Makam, 2023).
• Patentability and Non-Obviousness: AI's ability to process vast datasets and generate novel compositions may lead to questions about the non-obviousness of these inventions. The traditional standard of a "person having ordinary skill in the art" may not be sufficient to evaluate AI-generated inventions, as AI can produce results that might seem obvious to its computational capabilities but not to human researchers (Fabris, 2020).
• Data Ownership and Licensing: The use of AI in drug development involves large datasets, raising issues of data ownership and licensing. Indian IP laws need to address these concerns to ensure that data used by AI systems is legally obtained and that the rights of data owners are protected (Poddar & Rao, 2024) (Pitel et al., 2024).

Opportunities for Legal and Policy Reforms

• Sui Generis Rights: There is a growing recognition of the need for sui generis rights to protect AI-generated outputs. Such rights could provide a tailored approach to IP protection, recognizing the unique nature of AI-generated inventions and ensuring that they are adequately protected under the law (Picht & Thouvenin, 2023).
• Collaborative Frameworks: A collaborative approach involving policymakers, industry stakeholders, and legal experts is essential to navigate the complexities of AI and IP. This could lead to the development of a stable IP regime that supports innovation while addressing the unique challenges posed by AI technologies (Poddar & Rao, 2024).
• International Cooperation: Given the global nature of AI and pharmaceutical industries, international cooperation is crucial. Aligning Indian IP laws with international standards and practices can facilitate the commercialization of AI-generated pharmaceuticals on a global scale (Napitupulu et al., 2023).

Ethical and Practical Considerations

Indian Courts and AI Pharma IP

Challenges in AI-Generated Pharma IP

• Inventorship and Ownership: AI's role in drug discovery raises questions about who qualifies as an inventor. Traditional IP laws require a human inventor, but AI systems like DABUS challenge this notion by autonomously generating inventions (Choi, 2022). Indian courts must decide whether AI can be recognized as an inventor or if the human developers or operators should hold the rights.
• Patentability and Protection: The criteria for patentability, such as novelty and non-obviousness, are complicated by AI's ability to generate vast amounts of data and potential inventions. Courts need to determine how these criteria apply to AI-generated inventions and whether existing laws are sufficient to protect such innovations (Poddar & Rao, 2024) (Choi, 2022).
• Data Ownership and Exclusivity: AI systems rely on large datasets, raising issues of data ownership and exclusivity. Indian courts must address how data used by AI in drug development is protected and who holds the rights to AI-generated data outputs (Kimball & Ragavan, 2022).

Legal Framework and Court Decisions

• Current Legal Framework: India's IP laws, including the Patents Act, do not explicitly address AI-generated inventions, creating ambiguity in legal interpretations. Courts are tasked with interpreting these laws in light of AI advancements, potentially setting precedents for future cases (Pai et al., 2024) (Bratajaya, 2024).
• Judicial Precedents: Indian courts have the opportunity to set important precedents in AI-related IP cases. These decisions will influence not only national but also international IP jurisprudence, as other jurisdictions look to India for guidance on handling similar issues (Solow-Neiderman, 2024).

Ethical and Societal Considerations

• Balancing Innovation and Protection: Courts must balance the need to protect IP rights with the promotion of innovation. Overly stringent IP protections could stifle AI-driven advancements, while too lenient protections might undermine the incentives for innovation (Mahingoda, 2023) (Ni, 2024).
• Ethical Implications: The ethical dimensions of AI in pharma, such as accountability and transparency, are critical. Courts must consider these factors when adjudicating cases, ensuring that AI applications in healthcare do not compromise patient safety or ethical standards (Pai et al., 2024).

Policy Gaps in AI-Driven Pharma Under India’s AI Framework

AI-Generated Pharmaceuticals and Existing Frameworks

• AI has revolutionized drug discovery and development, offering significant advancements in precision medicine and patient outcomes. However, current regulatory frameworks often fall short in addressing AI's unique characteristics, such as real-time learning and algorithmic transparency (Warokar & Lote, 2024) (Pitel et al., 2024).
• Ethical concerns, including data privacy, algorithmic bias, and equitable access to AI-driven therapies, are critical issues that existing policies must address to ensure responsible AI innovation in pharmaceuticals (Pitel et al., 2024) (Dara & Azarpira, 2025).
• The integration of AI in drug development requires updated regulatory norms to accommodate AI's dynamic nature and ensure patient safety and data protection (Warokar & Lote, 2024) (Mirakhori & Niazi, 2024).

Policy Gaps and Proposed Amendments

• India's Draft National Policy on AI emphasizes the need for comprehensive data governance and ethical AI practices. However, it lacks specific guidelines for AI in pharmaceuticals, highlighting a gap in addressing sector-specific challenges (Bansal & Jain, 2023).
• Proposed amendments should include clear guidelines for AI validation processes, transparency in AI decision-making, and mechanisms to mitigate biases in AI algorithms (Olorunsogo et al., 2024) (Yu et al., 2024).
• Collaborative efforts between public and private sectors are essential to develop infrastructure, workforce skills, and regulatory reforms that support AI integration in the pharmaceutical industry (Kimta & Dogra, 2024).

Ethical Considerations in AI-Driven Drug Development

• Ethical frameworks must prioritize patient data privacy, informed consent, and accountability in AI-driven drug development. Ensuring transparency in AI operations is crucial to maintaining public trust and ethical integrity (Olorunsogo et al., 2024) (Yu et al., 2024). The use of copyrighted academic datasets in AI-driven drug development raises ethical questions about fair use under Indian copyright law, necessitating clear guidelines to ensure compliance with educational and research exemptions (Kumar & Yadav, 2025).
• Intellectual property rights for AI-generated pharmaceuticals present another challenge, requiring updates to existing IP laws to accommodate AI-assisted inventions and creations (Makam, 2023).
• Engaging diverse stakeholders, including ethicists, policymakers, and industry experts, is vital to align AI technologies with societal values and ethical norms (Dara & Azarpira, 2025) (Yu et al., 2024).

AI Patents, Drug Affordability, and Compulsory Licensing

Impact of AI-Driven Patents on Drug Affordability

• Increased Efficiency and Innovation: AI technologies have revolutionized drug discovery and development by enhancing efficiency and reducing costs. This has led to faster R&D processes, which could theoretically lower drug prices. However, the current patent system may not reflect these efficiencies, as it still allows for extended exclusivity periods that keep prices high (Kavusturan, 2020) (Sabet et al., 2024).
• Patentability Challenges: AI-generated innovations raise questions about patentability, as traditional patent laws may not adequately address inventions created by AI. This uncertainty can lead to prolonged patent disputes, potentially delaying the entry of affordable generics into the market (Sabet et al., 2024).
• Exclusivity and Market Dynamics: The exclusivity granted by patents, even those driven by AI, can lead to monopolistic pricing. This is particularly problematic in developing countries where high prices limit access to life-saving medicines (Li, 2011) (Malmqvist, 2021).

Compulsory Licensing and AI-Generated Pharma Innovations

• Legal Framework and Flexibility: Compulsory licensing (CL) is a legal tool that allows governments to authorize the production of patented drugs without the consent of the patent holder, primarily to address public health needs. This mechanism can be particularly effective in ensuring access to essential medicines in resource-poor settings (Guennif, 2017) (Kuanpoth, 2008). Compulsory licensing remains a critical mechanism in India to enhance access to essential medicines, particularly in the context of AI-driven pharmaceutical innovations, by allowing generic production of patented drugs under specific conditions (Singh, Singh, Prakash, & Yadav, 2025).
• Impact on Drug Accessibility: Studies indicate that CL does not negatively impact the availability, affordability, or quality of drugs. Instead, it can enhance access to essential medicines, such as antiretroviral drugs, by bypassing patent restrictions (Guennif, 2017) (Kuanpoth, 2008).
• Balancing Innovation and Access: While CL can improve access, it may also discourage innovation if not carefully managed. The challenge lies in balancing the need for affordable medicines with the incentives required for continued pharmaceutical innovation, especially in the context of AI-driven discoveries (Pogge, 2010) (Avallone, 2022).

Ethical Challenges in AI-Driven Healthcare

Ensuring Equitable Access to AI-Generated Medicines

• Algorithmic Bias and Inclusivity: AI systems can perpetuate biases if trained on non-diverse datasets, leading to inequitable access to AI-generated medicines. Ensuring inclusivity in data sets used for training AI models is crucial to prevent biases in decision-making and to promote fair distribution of AI-powered treatments (Pitel et al., 2024) (Yu et al., 2024).
• Regulatory Frameworks: The dynamic regulatory environment poses challenges to equitable access. Continuous adaptation of regulatory frameworks is necessary to ensure that AI-driven therapies are accessible to all, regardless of socioeconomic status (Pitel et al., 2024).
• Global Health Equity: AI has the potential to improve global health equity by making advanced medical treatments more widely available. However, this requires a concerted effort to address existing health disparities and ensure that AI technologies do not exacerbate them (“Ethical Challenges and Opportunities in AI-Driven Healthcare,” 2025).

Addressing Data Privacy in AI-Driven Research

• Data Privacy and Security: The integration of AI in healthcare raises significant concerns about patient data privacy and security. Theoretical models and practical approaches, such as the 'Integrated Security and Ethics Model,' are essential to mitigate risks and enhance protections (Gawankar et al., 2024).
• Informed Consent and Transparency: Traditional informed consent models are inadequate in the context of large-scale data environments. Evolving these models to include AI-powered oversight processes can help maintain privacy, equity, and trust among patients (Evans & Bihorac, 2024).
• Regulatory Compliance: Compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) is crucial to address liability in cases of AI errors and to protect patient data (Yu et al., 2024) (Momani, 2025).

Ethical Frameworks and Governance

• Ethical Frameworks: Developing robust ethical frameworks is essential to guide the development and implementation of AI technologies in healthcare. These frameworks should focus on transparency, fairness, and accountability to uphold patient rights and ensure responsible AI integration (Gunawan & Wiputra, n.d.) (Eacersall et al., 2024).
• Stakeholder Engagement: Engaging diverse stakeholders, including policymakers, healthcare providers, and AI developers, is crucial for aligning AI development with ethical norms and addressing practical clinical needs (Yu et al., 2024).
• Explainable AI: Developing explainable AI systems can foster trust and transparency, allowing healthcare professionals and patients to understand AI-driven decisions and their implications (Journal, 2024).

Global Approaches to AI Inventorship and Patentability

Approaches to AI Inventorship and Patentability

• United States: The US patent system currently does not recognize AI as an inventor. The case of Thaler v. Iancu highlighted this limitation, where the court ruled that only natural persons could be recognized as inventors under US law (Mutale, 2024). The US does allow patents for software that demonstrate a technical effect, which can include AI-based inventions, provided they meet the criteria of novelty, non-obviousness, and utility (Bhardwaj & Gupta, n.d.).
• European Union: Similar to the US, the EU does not recognize AI as an inventor. However, the EU has a more flexible approach to software patents, allowing them if they provide a technical contribution beyond a mere computer program (Mutale, 2024). The EU's approach emphasizes the technical character of the invention, which can facilitate the patenting of AI-related innovations (“Intellectual Property Rights for Software, Artificial Intelligence and Computer Related Inventions: A Comparative Analysis,” 2024).
• Other Jurisdictions: In South Africa, there is a growing discourse on the need for a sui generis system to address AI inventorship, given the limitations of current patent laws in recognizing AI-generated inventions (Mutale, 2024). This reflects a broader trend in some jurisdictions to consider new legal frameworks tailored to the unique challenges posed by AI technologies (Kazimi & Thalwal, 2024).

Lessons for India’s IP Regime

• Current Challenges: India's patent law explicitly excludes software and algorithms from patentability, which poses a significant barrier to AI-based inventions (Bhardwaj & Gupta, n.d.). This exclusion reflects a more conservative approach compared to the US and EU, potentially stifling innovation in AI technologies.
• Potential Reforms: To foster innovation, India could consider revising its patent laws to allow for the patentability of AI-based inventions that demonstrate a technical effect, similar to the approaches in the US and EU (“Artificial Intelligence and Its Patentability: A Comparative Study Between India,UK, and USA,” 2023). This would involve developing clear guidelines for assessing the novelty and non-obviousness of AI-generated inventions (Artificial Intelligence and Inventorship Under the Patent Law Regime: Practical Development from Common Law Jurisdictions, 2023).
• Inventorship Considerations: India could also explore the possibility of recognizing AI as a co-inventor or developing a sui generis system to address the unique challenges of AI inventorship. This would require a careful balance between protecting human inventors' rights and acknowledging AI's role in the invention process (“Emerging Technologies and Intellectual Property Rights: A Cross-Jurisdictional Examination of AI and Patent Laws in India and the USA,” 2023) (Mutale, 2024).

TRIPS, AI, and Indian Pharma

Influence of TRIPS on Indian IP Laws

Harmonization Challenges for AI-Generated Pharma Innovations

• Inventorship and Ownership: AI's role in drug discovery complicates traditional notions of inventorship, as AI systems can independently generate novel compounds. Current IP laws, including those influenced by TRIPS, do not adequately address AI-generated inventions (Poddar & Rao, 2024).
• Data Ownership and Licensing: The cross-border nature of AI data flows presents challenges in harmonizing data protection and ownership laws, which are crucial for AI-driven pharmaceutical innovations (Khan, 2024).
• Regulatory Divergence: Different interpretations of TRIPS provisions by member countries, including India, create inconsistencies in patentability criteria and enforcement, complicating the global harmonization of AI-related IP laws (Maria, n.d.) (Kapczynski, 2009).