The Drug Design Data Resource (D3R) is a community-oriented initiative that collects and uses scientific data to test and advance the state of the art in computer aided drug design (CADD) technologies by holding community-wide blinded prediction challenges. D3R focuses primarily on the core CADD challenges of ligand pose prediction and ligand affinity prediction or ranking, thus key data types are high-quality ligand-protein cocrystal structures and measured ligand-protein affinities. From 2014 to the present, D3R has been funded as the sole NIH Drug Docking and Screening Data Resource under a NIH U01 cooperative agreement. In this time, Professors Rommie E. Amaro and Michael K. Gilson, together with Professor Stephen Burley at the Research Collaboratory for Structural Biology Protein Data Bank (RCSB PDB), have built D3R into a well-networked, community-centered, international hub for community challenges in pose-prediction and ligand-ranking. In this talk, I will discuss the broad conclusions about the characteristics of successful CADD methods that the challenges held by D3R have allowed, as well as describe ongoing opportunities and new technology developments that will hopefully allow us to overcome current methodological obstacles.

Ligand-protein pose and affinity predictions: New perspectives and lessons learned from the drug design data resource (D3R)

2019 Fall National Meeting

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