Elbadawi, M;
Gaisford, S;
Basit, AW;
(2021)
Advanced machine-learning techniques in drug discovery.
Drug Discovery Today
, 26
(3)
pp. 769-777.
10.1016/j.drudis.2020.12.003.
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Abstract
The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery.
| Type: | Article |
|---|---|
| Title: | Advanced machine-learning techniques in drug discovery |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.drudis.2020.12.003 |
| Publisher version: | https://doi.org10.1016/j.drudis.2020.12.003 |
| Language: | English |
| Additional information: | Copyright © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Machine Learning; Reinforcement Learning; Transfer Learning; Multi-task Learning; Bayesian Neural Networks; Hybrid Quantum-Machine Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10118933 |
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