4d-QSAR: New Perspectives in Drug Design

QSAR relationships are helpful in understanding and explaining the mechanism of drug action at the molecular level and allow the design and development of new compounds presenting desirable biological properties. 3D-QSAR formalisms, such as comparative molecular field analysis (CoMFA), use a set of compounds to generate 3D descriptors for building partial least squares (PLS) models, and provide relevant information for developing ligandbased drug design. The classical QSAR methods use as descriptors experimentally-derived molecular parameters and those calculated from the molecular connection table. The models obtained in the 4D-QSAR approach were also validated applying the y-randomization and LNO cross-validation in order to evaluate their reliability and robustness. Good QSAR models must have an average value of q² LNO, q² LNO, close to the q² LOO and standard deviation for each N should not exceed 0.1. It is recommended that N represents a significant fraction of samples (like leave-30%-out) in a satisfactory LNO test. A new formalism that takes advantage of GROMACS MD frames to build interaction energy models was presented in this study. The LQTA-QSAR formalism can be adapted to reach the user needs on building 4D-QSAR models, using a recent algorithm for variable selection, OPS, which has proved to be fast and capable of providing suitable variables for a PLS multivariate analysis. Thus, the best OPS-PLS models have demonstrated robustness and a good predictability for both investigated sets, using unbound ligands in a solvent medium.