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The performance of several structure-based design (SBD) approaches in predicting the binding affinity of diverse small molecule inhibitors co-crystallized to human renin was assessed to ascertain the modeling tool and method of choice required when dealing with structure-based lead optimization projects. Most of the SBD approaches investigated here were able to provide qualitative guidance, but quantitative accuracy as well as decisive discrimination between [in]actives is still not within reach. Such an outcome suggests that the current methods need improvement to capture the overall physics of the binding phenomenon for consistent applications in a lead optimization setting., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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 © 2011 Elsevier Ltd. All rights reserved.)