New scoring functions for virtual screening from molecular dynamics simulations with a quantum-refined force-field (QRFF-MD). Application to cyclin-dependent kinase 2.

A recently introduced new methodology based on ultrashort (50-100 ps) molecular dynamics simulations with a quantum-refined force-field (QRFF-MD) is here evaluated in its ability both to predict protein-ligand binding affinities and to discriminate active compounds from inactive ones. Physically based scoring functions are derived from this approach, and their performance is compared to that of several standard knowledge-based scoring functions. About 40 inhibitors of cyclin-dependent kinase 2 (CDK2) representing a broad chemical diversity were considered. The QRFF-MD method achieves a correlation coefficient, R(2), of 0.55, which is significantly better than that obtained by a number of traditional approaches in virtual screening but only slightly better than that obtained by consensus scoring (R(2) = 0.50). Compounds from the Available Chemical Directory, along with the known active compounds, were docked into the ATP binding site of CDK2 using the program Glide, and the 650 ligands from the top scored poses were considered for a QRFF-MD analysis. Combined with structural information extracted from the simulations, the QRFF-MD methodology results in similar enrichment of known actives compared to consensus scoring. Moreover, a new scoring function is introduced that combines a QRFF-MD based scoring function with consensus scoring, which results in substantial improvement on the enrichment profile.

3D-QSAR CoMFA study on imidazolinergic I(2) ligands: a significant model through a combined exploration of structural diversity and methodology.

Displaying an unprecedented structural diversity, 119 I(2) ligands, and their pK(i) values, were collected and submitted to a comparative molecular fields analysis (CoMFA) study. They were discerned into three structural subsets (A, B, C), to explore the I(2) 3D-QSARs from finite structural systems (A, B, C) to more complex ones (AB, AC, BC, ABC). In addition, various key steps of the CoMFA methology were explored. The applied method used two pharmacophore templates and seven molecular field combinations (electrostatic, lipophilic, steric), as well as eight alignment methods (two point-by-point and six similarity-based variations). That way, 644 CoMFA models were obtained and further selected according to their predictive ability through two filters. The first filter was mainly based on the q(2), which internally evaluates the predictive ability from the training set. For the second filter, the predictive ability was externally evaluated through the prediction of test sets. Finally, one model was extracted from the whole data as the best. Indeed, it combines three features of upmost importance for the further design of ligands endowed with high I(2) affinity: structural diversity (n = 73), robustness (N = 9, r(2) = 0.96, s = 0. 28, F = 148), and a great fully assessed predictive ability (q(2) = 0.50, r(2)(test set) = 0.81, n(test set) = 46). On the basis of structural data and CoMFA isocontours, some elements of the I(2) tridimensional pharmacophore are also suggested.