Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …

Highlights•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint
and similarity analysis.•Machine learning models for virtual screening.•Future challenges …

The annotation of small molecules remains a major challenge in untargeted mass
spectrometry-based metabolomics. We here critically discuss structured elucidation …

Recently, a novel class of degree-based topological molecular descriptors was proposed,
the so-called Sombor indices. Within this study, the predictive and discriminative potentials …

The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural
networks such as deep neural networks or recurrent networks drive this area. Numerous …

Computer-aided drug discovery/design methods have played a major role in the
development of therapeutically important small molecules for over three decades. These …

There are thousands of papers published every year investigating the properties and
possible applications of ionic liquids. Industrial use of these exceptional fluids requires …

Drug-induced liver injury (DILI) has been the single most frequent cause of safety-related
drug marketing withdrawals for the past 50 years. Recently, deep learning (DL) has been …

Motivation Identification of blood–brain barrier (BBB) permeability of a compound is a major
challenge in neurotherapeutic drug discovery. Conventional approaches for BBB …