Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

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 …

Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …

Abstract Background Traditional Chinese medicine (TCM), as a significant part of the global
pharmaceutical science, the abundant molecular compounds it contains is a valuable …

In computer-aided drug discovery (CADD), virtual screening (VS) is used for comparing a
library of compounds against known active ligands to identify the drug candidates that are …

Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due
to the limited computational resources in practical applications and is a crucial basis for drug …

Prediction of protein–ligand binding affinities is a central issue in structure-based computer-
aided drug design. In recent years, much effort has been devoted to the prediction of the …

The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

We propose a novel machine-learning-based scoring function for drug discovery that
incorporates ligand and protein structural information into a knowledge-based PMF score …