The use of deep machine learning (ML) in protein structure prediction has made it possible
to easily access a large number of annotated conformations that can potentially compensate …

Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing
the creation of molecules that target specific binding pockets. Recent advances in this area …

Generating low-energy molecular conformers is a key task for many areas of computational
chemistry, molecular modeling and cheminformatics. Most current conformer generation …

Predicting the native poses of ligands correctly is one of the most important steps towards
successful structure-based drug design. Binding affinities (BAs) estimated by traditional …

Background PubChem is a free and publicly available resource containing substance
descriptions and their associated biological activity information. PubChem3D is an …

Predicting energetically favorable 3-dimensional conformations of organic molecules
frommolecular graph plays a fundamental role in computer-aided drug discovery research …

Efficient design and screening of the novel molecules is a major challenge in drug and
material design. This paper focuses on a multi‐stage pipeline, in which several deep neural …

Molecular property prediction, crucial for early drug candidate screening and optimization,
has seen advancements with deep learning-based methods. While deep learning-based …

In this study, we systematically studied the energy distribution of bioactive conformations of
small molecular ligands in their conformational ensembles using ANI-2X, a machine …

Chemical space is impractically large, and conventional structure-based virtual screening
techniques cannot be used to simply search through the entire space to discover effective …