Artificial intelligence (AI) techniques have already been gradually applied to the entire drug
design process, from target discovery, lead discovery, lead optimization and preclinical …

Efficient molecular featurization is one of the major issues for machine learning models in
drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC …

Artificial intelligence (AI)-based drug design has great promise to fundamentally change the
landscape of the pharmaceutical industry. Even though there are great progress from …

Molecular descriptors are essential to not only quantitative structure-activity relationship
(QSAR) models but also machine learning–based material, chemical, and biological data …

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to
computer-aided drug design, but remains a challenging problem. To achieve efficient and …

Protein–protein interactions (PPIs) play a significant role in nearly all cellular and biological
activities. Data-driven machine learning models have demonstrated great power in PPIs …

Drug-target binding affinity prediction plays a key role in the early stage of drug discovery.
Numerous experimental and data-driven approaches have been developed for predicting …

Artificial intelligence (AI) based drug design has demonstrated great potential to
fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug …

Molecular descriptors are essential to quantitative structure activity/property relationship
(QSAR/QSPR) models and machine learning models. Here we propose persistent path …

In drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a
pivotal task for lead optimization with acceptable on-target potency as well as …