Resource For Structure-Based Computational Drug Discovery And Design (RSCD3)
Building on several decades of work with the AutoDock Suite, the Resource for Structure-based Computational Drug Discovery and Design (RSCD3) aims to provide a user-facing infrastructure for novice and expert users to easily apply a diverse, modern toolset of methods to drug discovery and design applications.
RSCD3 is directed by Dr. Stefano Forli, PI of the Forli lab at Scripps Research.
AutoDock-GPU
Santos-Martins, D., et al. (2021). “Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.” Journal of Chemical Theory and Computation 17(2): 1060-1073. DOI: 10.1021/acs.jctc.0c01006
AutoDock-Vina
Trott, O. and A. J. Olson (2010). “AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.” J Comput Chem 31(2): 455-461. DOI: 10.1002/jcc.21334
Eberhardt, J., et al. (2021). “AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.” Journal of Chemical Information and Modeling 61(8): 3891-3898. DOI: 10.1021/acs.jcim.1c00203
AutoDock FR &
AutoDock CrankPep
Ravindranath, P. A., et al. (2015). “AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.” PLOS Computational Biology 11(12): e1004586. DOI: 10.1371/journal.pcbi.1004586
Zhang, Y. and M. F. Sanner (2019). “AutoDock CrankPep: combining folding and docking to predict protein–peptide complexes.” Bioinformatics 35(24): 5121-5127. DOI: 10.1093/bioinformatics/btz459
News & Events
RSCD3 is extending its support for the community through documentation and self-taught tutorials as well as presenting workshops at national conferences, community discussion groups, and expert guidance for showcasing more recent and advanced protocols.
Visit Our Campus At Scripps Research, La Jolla, CA
This resource is funded by a National Institute of General Medical Sciences grant: R24GM145962.
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