Natural products (NPs) are primarily recognized as privileged structures to interact with
protein drug targets. Their unique characteristics and structural diversity continue to marvel …

This review seeks to provide a timely survey of the scope and limitations of cheminformatics
methods in natural product‐based drug discovery. Following an overview of data resources …

The ultimate goal of drug design is to find novel compounds with desirable pharmacological
properties. Designing molecules retaining particular scaffolds as their core structures is an …

Whether AI explanations can help users achieve specific tasks efficiently (ie, usable
explanations) is significantly influenced by their visual presentation. While many techniques …

Control of fungal pathogens is increasingly problematic due to the limited number of
effective drugs available for antifungal therapy. Conventional antifungal drugs could also …

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a
challenging procedure requiring lots of time and resources. Therefore, computer-aided drug …

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon
in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of …

During the last decades, there is a vast data explosion in bioinformatics. Big data centres are
trying to face this data crisis, reaching high storage capacity levels. Although several …

With the increase in applications of machine learning methods in drug design and related
fields, the challenge of designing sound test sets becomes more and more prominent. The …

The exploration of biologically relevant chemical space for the discovery of small bioactive
molecules present in marine organisms has led not only to important advances in certain …