Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges
as a transformative force, bridging the realms of biology and technology. This paper …

De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …

Photopharmacology is an attractive approach for achieving targeted drug action with the use
of light. In photopharmacology, molecular photoswitches are introduced into the structure of …

The drug discovery process is a rocky path that is full of challenges, with the result that very
few candidates progress from hit compound to a commercially available product, often due …

Nanozymes, nanomaterial-based artificial enzymes, exhibit potential for emulating the
catalytic functions inherent in enzymes. Nanozymes have advantages such as low cost …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …