Computational approaches that rely on ligand-based information for lead discovery and
optimization are often required to spend considerable resources analyzing compounds with …

Background The number of protein targets with a known or predicted tri-dimensional
structure and of drug-like chemical compounds is growing rapidly and so is the need for new …

A key challenge in conformer sampling is finding low-energy conformations with a small
number of energy evaluations. We recently demonstrated the Bayesian Optimization …

Abstract Structure-based virtual screening (SBVS) via docking has been used to discover
active molecules for a range of therapeutic targets. Chemical and protein data sets that …

Conformer generation has important implications in cheminformatics, particularly in
computational drug discovery where the quality of conformer generation software may affect …

Machine learning-based protein structure prediction algorithms, such as RosettaFold and
AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of …

Structure-based virtual screening utilizes molecular docking to explore and analyze ligand–
macromolecule interactions, crucial for identifying and developing potential drug candidates …

Background PubChem, an open archive for the biological activities of small molecules,
provides search and analysis tools to assist users in locating desired information. Many of …

Ligand molecule conformation generation is a critical challenge in drug discovery. Deep
learning models have been developed to tackle this problem, particularly through the use of …

Motivation Increasing efforts are being made in the field of machine learning to advance the
learning of robust and accurate models from experimentally measured data and enable …