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Grzybowski, Bartosz Andrzej
첨단연성물질 연구단
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Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks

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Title
Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks
Author(s)
Wiktor Beker; Agnieszka Wołos; Sara Szymkuć; Bartosz A. Grzybowski
Publication Date
2020-08
Journal
Nature Machine Intelligence, v.2, no.8, pp.457 - 465
Publisher
SPRINGERNATURE
Abstract
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.Triaging unpromising lead molecules early in the drug discovery process is essential for accelerating its pace while avoiding the costs of unwarranted biological and clinical testing. Accordingly, medicinal chemists have been trying for decades to develop metrics—ranging from heuristic measures to machine-learning models—that could rapidly distinguish potential drugs from small molecules that lack drug-like features. However, none of these metrics has gained universal acceptance and the very idea of ‘drug-likeness’ has recently been put into question. Here, we evaluate drug-likeness using different sets of descriptors and different state-of-the-art classifiers, reaching an out-of-sample accuracy of 87–88%. Remarkably, because these individual classifiers yield different Bayesian error distributions, their combination and selection of minimal-variance predictions can increase the accuracy of distinguishing drug-like from non-drug-like molecules to 93%. Because total variance is comparable with its aleatoric contribution reflecting irreducible error inherent to the dataset (as opposed to the epistemic contribution due to the model itself), this level of accuracy is probably the upper limit achievable with the currently known collection of drugs
URI
https://pr.ibs.re.kr/handle/8788114/9032
DOI
10.1038/s42256-020-0209-y
ISSN
2522-5839
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > 1. Journal Papers (저널논문)
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