Improving Quantitative Structure−Activity Relationships through Multiobjective Optimization

Abstract

A multiobjective optimization algorithm was proposed for the automated integration of structure- and ligand-based molecular design. Driven by a genetic algorithm, the herein proposed approach enabled the detection of a number of trade-off QSAR models accounting simultaneously for two independent objectives. The first was biased toward best regressions among docking scores and biological affinities; the second minimized the atom displacements from a properly established crystal-based binding topology. Based on the concept of dominance, 3D QSAR equivalent models profiled the Pareto frontier and were, thus, designated as nondominated solutions of the search space. K-means clustering was, then, operated to select a representative subset of the available trade-off models. These were effectively subjected to GRID/GOLPE analyses for quantitatively featuring molecular determinants of ligand binding affinity. More specifically, it was demonstrated that a) diverse binding conformations occurred on the basis of the ligand ability to profitably contact different part of protein binding site; b) enzyme selectivity was better approached and interpreted by combining diverse equivalent models; and c) trade-off models were successful and even better than docking virtual screening, in retrieving at high sensitivity active hits from a large pool of chemically similar decoys. The approach was tested on a large series, very well-known to QSAR practitioners, of 3-amidinophenylalanine inhibitors of thrombin and trypsin, two serine proteases having rather different biological actions despite a high sequence similarity.

Studies on the antiplatelet and antithrombotic profile of anti-inflammatory coumarin derivatives

“…The multi-target potency 51 of these compounds could be further used to design new series of promising antiplatelet and antithrombotic agents.…”

REACH and in silico methods: an attractive opportunity for medicinal chemists

“…Alongside with reprotoxicity, the exposure to EDs is considered responsible for typical Western world diseases, such as sex hormone-dependent cancers, obesity, diabetes, cardiovascular complications, and immune disorders. In this respect, advanced computational methods (i.e., shapebased screening, pharmacophore mapping, and docking simulations) [33,34], aiming to identify bioactive compounds from millions of available substances, can be profitably applied to screen potential ED chemicals [35]. An example ( Figure 3B) is the recent successful study based solely on virtual screening strategies that enabled the identification of the first industrial chemical (i.e., AB110873) disrupting corticosteroid action [36].…”

Screening of benzamidine-based thrombin inhibitors via a linear interaction energy in continuum electrostatics model

“…The presumed active conformations of inhibitors 13-29 (Table 2a-d) were instead manually modeled by biasing the prevalent binding modes of similar compounds occurring in X-ray complexes. This task required efforts and expertise acquired in modeling serine protease inhibitors over recent years [12][13][14]. To avoid inadvertent actions when deriving the molecular alignment, a careful delineation of the residues encompassing the binding site as well as of the molecular room effectively available was rigorously taken into account.…”

“…Of these three purposes, the last was indeed the most difficult as no statistically significant correlation between measured affinity and scoring function has so far emerged [10,11]. Recently, some of us approached this issue by devising a two term fitness function: the first accounted simultaneously for best regression among experimental affinity and docking score and the second for minimal atom displacements from a given crystallographic binding hypothesis [12][13][14]. However, despite the enormous progress in improving and calibrating scoring functions, the accurate prediction of binding affinity still remains an unmet goal.…”