3-D QSAutogrid/R: An Alternative Procedure To Build 3-D QSAR Models. Methodologies and Applications

Abstract

Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.

Synthesis, biological evaluation and docking analysis of a new series of methylsulfonyl and sulfamoyl acetamides and ethyl acetates as potent COX-2 inhibitors

“…The cross-docking protocol employed as much as possible the available experimental structural information representing the basis for future applications such as development of predictive 3-D QSAR and/or COMBINEr models. 23,24 The latter, could be used as external scoring functions to fill the lack of correct activity prediction trend by the associated docking program scoring function.…”

Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

“…Recently, an alternative procedure to build 3-D QSAR models, named 3-D QSAutogrid/R [38], was reported. The new process was confirmed to be a useful tool for highlighting the driving ligand-protein interactions [38][39][40], deriving quantitative pharmacophore models [41], and identifying new molecular scaffolds during a virtual screening (VS) investigation [42].…”

“…Recently, an alternative procedure to build 3-D QSAR models, named 3-D QSAutogrid/R [38], was reported. The new process was confirmed to be a useful tool for highlighting the driving ligand-protein interactions [38][39][40], deriving quantitative pharmacophore models [41], and identifying new molecular scaffolds during a virtual screening (VS) investigation [42]. Herein, as a completion of a first SB 3-D QSAR protocol [43] which proved its usefulness in finding a new VEGFR-2 lead compound [20], named 2f (a thieno [3,2-d]pyrimidinone derivative which inhibits VEGFR-2 at lM concentration, Supplementary Material Figure SM1), definitive partial least square (PLS) based 3-D QSAR models were derived, deeply described and robustly validated.…”

“…Herein, as a completion of a first SB 3-D QSAR protocol [43] which proved its usefulness in finding a new VEGFR-2 lead compound [20], named 2f (a thieno [3,2-d]pyrimidinone derivative which inhibits VEGFR-2 at lM concentration, Supplementary Material Figure SM1), definitive partial least square (PLS) based 3-D QSAR models were derived, deeply described and robustly validated. The training set molecules (see Training Set Selection section) were: (1) extracted from all the VEGFR-2-inhibitor complexes available from the Protein Data Bank (PDB) [44], (2) submitted to a minimization protocol (See Training Set Inhibitor Preparation section) and (3) used to build the 3-D QSAR models, by means of the 3-D QSAutogrid/R [38] procedure. Along with the classical cross-validation (CV) techniques (Leave One Out, K-Fold, MonteCarlo and Y-Scrambling), 10 different external test sets were used to estimate the models' predictive capability.…”

“…Many of the studies performed docking studies on the training and test set molecules using a variety of docking software (AutoDock [36], Sybyl [21,37], Surflex-Dock [35], MacroModel [31], GOLD [28] ), but each study made use of only one available crystal structure of VEGFR-2. Herein, by means of the 3-D QSAutogrid/R procedure [38], the very first general structure-based 3-D QSAR (SB 3-D QSAR) models has been developed using all the available experimental data for VEGFR-2 as training set, and externally validated with 262 molecules with unknown binding modes, composing ten different external test sets. Both internal and external assessments confirmed the usefulness of these models as a comprehensive basis for study and design novel VEGFR-2 inhibitors.…”