POSIT: Flexible Shape-Guided Docking For Pose Prediction

We present a new approach to structure-based drug design (POSIT) rigorously built on the simple concept that pose prediction is intimately coupled to the quality and availability of experimental structural data. We demonstrate the feasibility of the approach by performing retrospective analyses on three data sets designed to explore the strengths and weaknesses of POSIT relative to existing methods. We then present results documenting 2.5 years of prospective use of POSIT across a variety of structure-based industrial drug-discovery research projects. We find that POSIT is well-suited to guiding research decision making for structure-based design and, in particular, excels at enabling lead-optimization campaigns. We show that the POSIT framework can drive superior pose-prediction performance and generate results that naturally lend themselves to prospective decision making during lead optimization. We believe the results presented here are (1) the largest prospective validation of a pose prediction method reported to date (71 crystal structures); (2) provide an unprecedented look at the scope of impact of a computational tool; and (3) represent a first-of-its-kind analysis. We hope that this work inspires additional studies that look at the real impact and performance of computational research tools on prospective drug design.