Issue 23, 2021

DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Abstract

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0.

Graphical abstract: DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Supplementary files

Article information

Article type
Edge Article
Submitted
10 jan 2021
Accepted
06 mei 2021
First published
08 mei 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2021,12, 8036-8047

DeepFrag: a deep convolutional neural network for fragment-based lead optimization

H. Green, D. R. Koes and J. D. Durrant, Chem. Sci., 2021, 12, 8036 DOI: 10.1039/D1SC00163A

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