The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

A typical drug discovery project involves identifying active compounds with significant
binding potential for selected disease-specific targets. Experimental high-throughput …

Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for
molecular characterization. Historically, topological fingerprints were developed for …

A few decades ago, drug discovery and development were limited to a bunch of medicinal
chemists working in a lab with enormous amount of testing, validations, and synthetic …

Artificial Intelligence revolutionizes the drug development process that can quickly identify
potential biologically active compounds from millions of candidate within a short period. The …

Absolute binding free energy (ABFE) calculations with all-atom molecular dynamics (MD)
have the potential to greatly reduce costs in the first stages of drug discovery. Here, we …

In this review, we highlight recent applications of machine learning to virtual screening,
focusing on the use of supervised techniques to train statistical learning algorithms to …

Similarity searching is one of the most common techniques for ligand-based virtual
screening and involves scanning a chemical database to identify those molecules that are …

Statistical and machine learning approaches predict drug-to-target relationships from 2D
small-molecule topology patterns. One might expect 3D information to improve these …

Molecular modeling frequently constructs classification models for the prediction of two‐
class entities, such as compound bio (in) activity, chemical property (non) existence, protein …