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De novo molecular design and generative models

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Meyers, Joshua, Fabian, Benedek, Brown, Nathan·2021·Drug Discovery Today
DOI 10.1016/j.drudis.2021.05.019
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Abstract

Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years.

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Metadata

Authors
Meyers, J., Fabian, B., & Brown, N.
Venue
Drug Discovery Today
Publisher
Elsevier BV
Published
2021-11
Volume
26
Issue
11
Pages
2707-2715
ISSN
1359-6446 (ISSN-L)
DOI
10.1016/j.drudis.2021.05.019
OpenAlex
W3165171933
Cited by
292
References
8
FWCI
23.12
Type
journal article
Language
en
Open access
hybrid
Licence

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