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.
journal_name
Drug Discov Todayjournal_title
Drug discovery todayauthors
Elbadawi M,Gaisford S,Basit AWdoi
10.1016/j.drudis.2020.12.003subject
Has Abstractpub_date
2020-12-05 00:00:00eissn
1359-6446issn
1878-5832pii
S1359-6446(20)30521-3pub_type
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journal_title:Drug discovery today
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