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 …

D ligand conformations are required for most ligand-based drug design methods, such as
pharmacophore modeling, shape-based screening, and 3-D QSAR model building. Many …

Computational approaches for predicting protein–ligand interactions can facilitate drug lead
discovery and drug target determination. We have previously developed a …

We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based
(MedusaDock) methods used both independently and in consensus for ranking the …

The prediction of binding poses and affinities is an area of active interest in computer-aided
drug design (CADD). Given the documented limitations with either ligand or structure based …

This issue contains a Perspective article by Dr. Richard Cramer, who is known by all as the
inventor of the popular 3D QSAR method CoMFA. A December 2010 search of Google …

The possible applicability of the new template CoMFA methodology to the prediction of
unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding …

Ligand based virtual screening (LBVS) approaches could be broadly divided into those
relying on chemical similarity searches and those employing Quantitative Structure‐Activity …

Since the advent of classical QSAR techniques, exemplified by Hansch [1], there has been
considerable progress in the development of molecular descriptors and chemometric …