Machine learning classifiers trained on class imbalanced data are prone to overpredict the
majority class. This leads to a larger misclassification rate for the minority class, which in …

The specificity of toxicant-target biomolecule interactions lends to the very imbalanced
nature of many toxicity datasets, causing poor performance in Structure–Activity …

Drug discovery studies have become increasingly expensive and time-consuming
processes. In the early phase of drug discovery studies, an extensive search has been …

Abstract Machine learning (ML) has demonstrated the promise for accurate and efficient
property prediction of molecules and crystalline materials. To develop highly accurate ML …

In recent years, protein–ligand interaction scoring functions derived through machine-
learning are repeatedly reported to outperform conventional scoring functions. However …

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 …

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 …

Increase in the number of new chemicals synthesized in past decades has resulted in
constant growth in the development and application of computational models for prediction …

Artificial intelligence (AI) has been widely applied in drug discovery with a major task as
molecular property prediction. Despite booming techniques in molecular representation …

Artificial intelligence (AI) has become a powerful tool in many fields, including drug
discovery. Among various AI applications, molecular property prediction can have more …