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

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.

Automated summary

Machine learning has been increasingly applied in the field of computer-aided drug discovery, showing notable advances in binding-affinity prediction, virtual screening, and QSAR. A deep convolutional neural network was used to predict appropriate fragments based on the structure of a receptor/ligand complex, with an efficiency of about 58% in selecting correct fragments from known ligands. The trained DeepFrag model and its associated software have been released under the Apache License, Version 2.0.