Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes.

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

High content pharmacophores from molecular fields: a biologically relevant method for comparing and understanding ligands.

The question of how and why a small molecule binds to a protein is central to ligand-based drug discovery. The traditional way of approaching these questions is pharmacophore analysis. However, pharmacophores as usually applied lack quantitation and subtlety. An improvement is to consider the electrostatic and steric fields of the ligand directly. Molecular fields provide a rich view of the potential interactions that a molecule can make and can be validated through experimental data on molecular interactions and through quantum mechanics calculations. A technique is presented in this review for comparing molecules using molecular fields and assigning similarity scores. This high information content method can be used to align molecules for SAR analysis, to determine the bioactive conformation from ligand data, and to screen large libraries of compounds for structurally unrelated actives. An extension to allow interactive exploration of chemistry space via bioisostere analysis is also reviewed. Examples from the literature showing the success of these methods are presented, and future directions discussed.

Better than random? The chemotype enrichment problem.

Chemotype enrichment is increasingly recognized as an important measure of virtual screening performance. However, little attention has been paid to producing metrics which can quantify chemotype retrieval. Here, we examine two different protocols for analyzing chemotype retrieval: "cluster averaging", where the contribution of each active to the scoring metric is proportional to the number of other actives with the same chemotype, and "first found", where only the first active for a given chemotype contributes to the score. We demonstrate that this latter analysis, common in the qualitative analysis used in the current literature, has important drawbacks when combined with quantitative metrics.

FieldScreen: virtual screening using molecular fields. Application to the DUD data set.

FieldScreen, a ligand-based Virtual Screening (VS) method, is described. Its use of 3D molecular fields makes it particularly suitable for scaffold hopping, and we have rigorously validated it for this purpose using a clustered version of the Directory of Useful Decoys (DUD). Using thirteen pharmaceutically relevant targets, we demonstrate that FieldScreen produces superior early chemotype enrichments, compared to DOCK. Additionally, hits retrieved by FieldScreen are consistently lower in molecular weight than those retrieved by docking. Where no X-ray protein structures are available, FieldScreen searches are more robust than docking into homology models or apo structures.