CSARdock.org

Welcome to CSAR

Please email results of the exercise to Dr. Carlson (carlsonh@umich.edu).

Michigan is honored to host the Community Structure-Activity Resource (CSAR)!
This effort aims to improve docking and scoring through participation of the entire scientific community. CSAR will disseminate experimental datasets of crystal structures and binding affinities for diverse protein-ligand complexes. Some datasets will be generated in house at Michigan while others will be collected from the literature or deposited by academic labs, national centers, and the pharmaceutical industry.

CSAR will organize workshops and benchmarks to examine various aspects of docking and scoring through community-wide participation. The 2010 Workshop will be held in August at the 240th ACS National Meeting (COMP) in Boston. Feedback is needed to design CSAR’s data sets that examine specific issues, so we have designed a Benchmark Exercise for the community. The dataset has been re-worked and currently contains ~350 structures. The deadline for contributing scores has been significantly extended to January 18th, 2010 to encourage more participation and to compensate for this change in the dataset and any inconvenience to participants.

Computational drug design techniques are very successful at enriching hit rates when identifying sets of compounds for experimental testing. However, it is not possible to reliably rank nanomolar-level compounds over those with micromolar affinities. To improve our approaches, we need better datasets to train scoring functions and develop new docking algorithms. That is the goal of the CSAR project.

We aim to provide the highest quality data for a diverse collection of proteins and small molecule ligands. We need input from the community in developing our target priorities. Ideal targets will have many high-quality crystal structures (apo and 10-20 bound to diverse ligands) and affinity data for ≥25 compounds that range in size, scaffold, and logP. It is best if the ligand set has several congeneric series that span a broad range of affinity, with low nanomolar to mid-micromolar being most desirable. We prefer Kd data over Ki data over IC50 data (no % activity data). Because of their low representation in the Protein Data Bank, more data for isomerases, ligases, lyases, and non-enzymes will be highly desirable. We also plan to provide forums on the website for software exchange and discussions devoted to improving various aspects of the field.

CSAR is funded by a U01 grant from the National Institute of General Medical Sciences. The original RFA can be found at http://grants.nih.gov/grants/guide/rfa-files/RFA-GM-08-008.html. Press releases about CSAR can be found at: