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

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of
drug development in the costly late stage, it has been widely recognized that drug ADMET …

Quantitative structure–activity relationship modeling is one of the major computational tools
employed in medicinal chemistry. However, throughout its entire history it has drawn both …

Jaguar is an ab initio quantum chemical program that specializes in fast electronic structure
predictions for molecular systems of medium and large size. Jaguar focuses on …

Drug-induced liver injury (DILI) is a patient-specific, temporal, multifactorial
pathophysiological process that cannot yet be recapitulated in a single in vitro model …

Drug metabolism can produce metabolites with physicochemical and pharmacological
properties that differ substantially from those of the parent drug, and consequently has …

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and
toxicity (T) modelling as a tool for rational drug design has received considerable attention …

Metabolomics has advanced significantly in the past 10 years with important developments
related to hardware, software and methodologies and an increasing complexity of …