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

As the global burden of antibiotic resistance continues to grow, creative approaches to
antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly …

Generative models are becoming a tool of choice for exploring the molecular space. These
models learn on a large training dataset and produce novel molecular structures with similar …

Predicting protein-ligand interactions using artificial intelligence (AI) models has attracted
great interest in recent years. However, data-driven AI models unequivocally suffer from a …

Eukaryotic ribosome and cap-dependent translation are attractive targets in the antitumor,
antiviral, anti-inflammatory, and antiparasitic therapies. Currently, a broad array of small …

Advances in computer hardware and the availability of high-performance supercomputing
platforms and parallel computing, along with artificial intelligence methods are successfully …

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …

Abstract Machine learning (ML) methods are used in cheminformatics processes to predict
the activity of an unknown drug and thus discover new potential antibacterial drugs. This …

Drug repurposing aims to find new uses for already existing and approved drugs. We now
provide a brief overview of recent developments in drug repurposing using machine …

Introduction After the initial wave of antibiotic discovery, few novel classes of antibiotics have
emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug …