Binding affinity prediction is frequently addressed using computational models constructed
solely with molecular structure and activity data. We present a hybrid structure-guided …

Computational methods for predicting ligand affinity where no protein structure is known
generally take the form of regression analysis based on molecular features that have only a …

Protein-ligand binding site prediction is a useful tool for understanding the functional
behaviour and potential drug-target interactions of a novel protein of interest. However, most …

Exhaustive exploration of molecular interactions at the level of complete proteomes requires
efficient and reliable computational approaches to protein function inference. Ligand …

The efficient and accurate quantification of protein− ligand interactions using computational
methods is still a challenging task. Two factors strongly contribute to the failure of docking …

The success of ligand docking calculations typically depends on the quality of the receptor
structure. Given improvements in protein structure prediction approaches, approximate …

Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic
models for binding affinity prediction. Here, we apply QMOD to a 3D-QSAR benchmark …

Computational methods for predicting ligand affinity where no protein structure is known
generally take the form of regression analysis based on molecular features that have only a …

The detection of ligand-binding sites is often the starting point for protein function
identification and drug discovery. Because of inaccuracies in predicted protein structures …

We present a reliable and accurate solution to the induced fit docking problem for protein–
ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking …