Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of
interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of …

Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and
development. Animal in vivo PK data as well as human and animal in vitro systems are …

Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery.
However, it is still critical for their adoption by the medicinal chemistry community to achieve …

Although computational predictions of pharmacokinetics (PK) are desirable at the drug
design stage, existing approaches are often limited by prediction accuracy and human …

The field of drug discovery has experienced a remarkable transformation with the advent of
artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and …

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the
challenges in drug discovery. Although traditional AI techniques generally have high …

Cocrystal engineering as an effective way to modify solid-state properties has inspired great
interest from diverse material fields while cocrystal density is an important property closely …

Abstract Graph Neural Networks (GNNs) excel in compound property and activity prediction,
but the choice of molecular graph representations significantly influences model learning …

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across
various sectors, with its impact being profoundly felt within the pharmaceutical and …

Atom typing is the first step for simulating molecules using a force field. Automatic atom
typing for an arbitrary molecule is often realized by rule-based algorithms, which have to …