Abstract Structure-based docking screens of large compound libraries have become
common in early drug and probe discovery. As computer efficiency has improved and …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

With the financial requirements and high time associated with bringing a commercial drug to
the market, the application of computer-aided drug design has been recognized as a …

We propose a novel deep learning approach for predicting drug–target interaction using a
graph neural network. We introduce a distance-aware graph attention algorithm to …

Molecular machine learning has been maturing rapidly over the last few years. Improved
methods and the presence of larger datasets have enabled machine learning algorithms to …

Alchemical free energy calculations are a useful tool for predicting free energy differences
associated with the transfer of molecules from one environment to another. The hallmark of …

Molecular “fingerprints” encoding structural information are the workhorse of
cheminformatics and machine learning in drug discovery applications. However, fingerprint …

Computational approaches to drug discovery can reduce the time and cost associated with
experimental assays and enable the screening of novel chemotypes. Structure-based drug …

Pharmaceutical research has successfully incorporated a wealth of molecular modeling
methods, within a variety of drug discovery programs, to study complex biological and …

Accurate determination of target-ligand interactions is crucial in the drug discovery process.
In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting …