Version 1
: Received: 24 May 2020 / Approved: 25 May 2020 / Online: 25 May 2020 (04:55:24 CEST)
How to cite:
Jordan, B. Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques. Preprints2020, 2020050408. https://doi.org/10.20944/preprints202005.0408.v1
Jordan, B. Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques. Preprints 2020, 2020050408. https://doi.org/10.20944/preprints202005.0408.v1
Jordan, B. Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques. Preprints2020, 2020050408. https://doi.org/10.20944/preprints202005.0408.v1
APA Style
Jordan, B. (2020). Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques. Preprints. https://doi.org/10.20944/preprints202005.0408.v1
Chicago/Turabian Style
Jordan, B. 2020 "Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques" Preprints. https://doi.org/10.20944/preprints202005.0408.v1
Abstract
The vastness of chemical-space constrains traditional drug-discovery methods to the organic laws that are guiding the chemistry involved in filtering through candidates. Leveraging computing with machine-learning to intelligently generate compounds that meet a wide range of objectives can bring significant gains in time and effort needed to filter through a broad range of candidates. This paper details how the use of Generative-Adversarial-Networks, novel machine learning techniques to format the training dataset and the use of quantum computing offer new ways to expedite drug-discovery.
Keywords
drug discovery; machine learning; in silico; pharmacology
Subject
Medicine and Pharmacology, Pharmacology and Toxicology
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.
