Toward accurate relative energy predictions of the bioactive conformation of drugs

TitleToward accurate relative energy predictions of the bioactive conformation of drugs
Publication TypeJournal Article
Year of Publication2009
AuthorsButler, KT, Luque FJ, Barril X
JournalJournal of computational chemistry
Pagination601 - 610
Date Published2009/03//
KeywordsDrug Design; Models, Molecular; Molecular Conformation; Pharmaceutical Preparations/chemistry; Structure-Activity Relationship; Thermodynamics
AbstractQuantifying the relative energy of a ligand in its target-bound state (i.e. the bioactive conformation) is essential to understand the process of molecular recognition, to optimize the potency of bioactive molecules and to increase the accuracy of structure-based drug design methods. This is, nevertheless, seriously hampered by two interrelated issues, namely the difficulty in carrying out an exhaustive sampling of the conformational space and the shortcomings of the energy functions, usually based on parametric methods of limited accuracy. Matters are further complicated by the experimental uncertainty on the atomic coordinates, which precludes a univocal definition of the bioactive conformation. In this article we investigate the relative energy of bioactive conformations introducing two major improvements over previous studies: the use sophisticated QM-based methods to take into account both the internal energy of the ligand and the solvation effect, and the application of physically meaningful constraints to refine the bioactive conformation. On a set of 99 drug-like molecules, we find that, contrary to previous observations, two thirds of bioactive conformations lie within 0.5 kcal mol(-1) of a local minimum, with penalties above 2.0 kcal mol(-1) being generally attributable to structural determination inaccuracies. The methodology herein described opens the door to obtain quantitative estimates of the energy of bioactive conformations and can be used both as an aid in refining crystallographic structures and as a tool in drug discovery.