Transfer Learning for Drug Discovery

Journal of Medicinal Chemistry
Chenjing CaiJianfeng Pei

Abstract

The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence-assisted drug discovery. One solution to this problem is to develop algorithms that can cope with relatively heterogeneous and scarce data. Transfer learning is a type of machine learning that can leverage existing, generalizable knowledge from other related tasks to enable learning of a separate task with a small set of data. Deep transfer learning is the most commonly used type of transfer learning in the field of drug discovery. This Perspective provides an overview of transfer learning and related applications to drug discovery to date. Furthermore, it provides outlooks on the future development of transfer learning for drug discovery.

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Citations

Dec 1, 2019·Drug Discovery Today. Technologies·Youjun XuJianfeng Pei
Nov 27, 2020·Advanced Drug Delivery Reviews·Rahul UpadhyaAdam J Gormley
Feb 4, 2021·Journal of Chemical Information and Modeling·Sankalp JainAlexey V Zakharov
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May 1, 2021·International Journal of Molecular Sciences·Bo QiangZhenming Liu
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Sep 24, 2021·Expert Opinion on Drug Discovery·Kit-Kay MakMallikarjuna Rao Pichika
Sep 28, 2021·Briefings in Bioinformatics·Xin AnYuanfang Guan

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