The interplay between life sciences and advancing technology drives a continuous cycle of
chemical data growth; these data are most often stored in open or partially open databases …

Understanding structure–activity relationships (SARs) for a given set of molecules allows
one to rationally explore chemical space and develop a chemical series optimizing multiple …

Reports of successful applications of machine learning (ML) methods in structure-based
virtual screening (SBVS) are increasing. ML methods such as convolutional neural networks …

Most chemical experiments are planned by human scientists and therefore are subject to a
variety of human cognitive biases, heuristics and social influences. These anthropogenic …

Undetected overfitting can occur when there are significant redundancies between training
and validation data. We describe AVE, a new measure of training–validation redundancy for …

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel
blockers is an essential part of the drug development and drug safety process in …

Data driven computational approaches to predicting protein–ligand binding are currently
achieving unprecedented levels of accuracy on held-out test datasets. Up until now …

Drug discovery pipelines typically involve high‐throughput screening of large amounts of
compounds in a search of potential drugs candidates. As a chemical space of small organic …

We explore the prediction of surfactant phase behavior using state-of-the-art machine
learning methods, using a data set for twenty-three nonionic surfactants. Most machine …

Prediction of the bound configuration of small-molecule ligands that differ substantially from
the cognate ligand of a protein co-crystal structure is much more challenging than re …