PT - JOURNAL ARTICLE AU - Harrison Green AU - David R. Koes AU - Jacob D. Durrant TI - DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization AID - 10.1101/2021.01.07.425790 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.01.07.425790 4099 - http://biorxiv.org/content/early/2021/01/08/2021.01.07.425790.short 4100 - http://biorxiv.org/content/early/2021/01/08/2021.01.07.425790.full AB - 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 6,500 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. A copy can be obtained free of charge from http://durrantlab.com/deepfragmodel.Competing Interest StatementThe authors have declared no competing interest.