Preprint Review Version 1 This version is not peer-reviewed

Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques

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. Preprints 2020, 2020050408 (doi: 10.20944/preprints202005.0408.v1). Jordan, B. Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques. Preprints 2020, 2020050408 (doi: 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.

Subject Areas

drug discovery; machine learning; in silico; pharmacology

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