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Highlights: [•] Surflex-dock poses show greater observed propensity of hydrogen bonding with NH of Ile 150 compared to Glide poses. [•] The docking scores of Surflex-dock poses correlate better with experimental binding affinities than Glide docked poses. [•] Pose prediction accuracy is improved when urea core constraints are imposed during the docking of the THP ligands. [•] 3-D QSAR CoMFA and CoMSIA models developed using Surflex-SIM (Sybyl-X) and Phase_Shape (Maestro) alignments have similar predictive powers. [ABSTRACT FROM AUTHOR]

Abstract: A new integrated computational workflow that couples the strength of the molecular overlay methods to achieve rapid and automated alignments along with 3D-QSAR techniques like CoMFA® and CoMSIA for quantitative binding affinity prediction is presented. The results obtained from such techniques are compared with rule-based Topomer CoMFA® method, where possible. The developed 3D-QSAR models were prospectively used to predict the affinities of new compounds designed through R-group deconvolution starting from the core chemical scaffold and subsequent virtual combinatorial library enumeration. The general applicability of the seamless in silico modeling workflow is demonstrated using several datasets reported for small molecule inhibitors of renin. [Copyright &y& Elsevier]

We introduce PHASE, a highly flexible system for common pharmacophore identification and assessment, 3D QSAR model development, and 3D database creation and searching. The primary workflows and tasks supported by PHASE are described, and details of the underlying scientific methodologies are provided. Using results from previously published investigations, PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds. [ABSTRACT FROM AUTHOR]