Niazi, S.K.; Mariam, Z. Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. Int. J. Mol. Sci.2023, 24, 11488.
Niazi, S.K.; Mariam, Z. Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. Int. J. Mol. Sci. 2023, 24, 11488.
Niazi, S.K.; Mariam, Z. Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. Int. J. Mol. Sci.2023, 24, 11488.
Niazi, S.K.; Mariam, Z. Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. Int. J. Mol. Sci. 2023, 24, 11488.
Abstract
In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
