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.


Getting Started

Beginners’ guides for molecular modeling, docking, and virtual screening.



Ongoing projects and new updates on AutoDock from RSCD3, and legacy programs from MGL.



Links to software, websites, databases and other useful resources for virtual screening.




Current version
AutoDock-GPU is the fastest full-service docking engine available at RSD3. It is a version of AutoDock4.2.6 accelerated by OpenCL and Cuda. It leverages the embarrassingly-parallelizable Lamarckian Genetic Algorithm of AutoDock by processing ligand-receptor poses in parallel over multiple compute units.

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



Current version
v1.1.2 (Obsolete)
AutoDock-Vina is one of the fastest and most widely used open-source docking engines. It is a turnkey computational docking program that is based on a simple scoring function and rapid gradient-optimization conformational search.

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

ADCP Latest
ADFR Current
AutoDockFR (or ADFR in short) is a protein-ligand docking program designed specifically, to include selective receptor flexibility and also supports covalent docking.Its custom Genetic Algorithm enables docking ligands with more rotatable bonds than AutoDock4.

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

AutoDock CrankPep or ADCP is an AutoDock docking engine specialized for docking peptides. It combines technology form the protein folding filed with an efficient representation of a rigid receptor as affinity grids to fold the peptide in the context of the energy landscape created by the receptor. It has been show to successfully re-dock peptides with up to 20 amino acids in length.

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

More from RSCD3
More from MGL (Obsolete)


Learn With Our

AutoDock Cookbook

Presented collaboratively by RSCD3, the AutoDock Cookbook provides practical examples of performing computational experiments and common tasks in drug discovery and design.

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For Beginners

Quick start


For Teachers

Free Materials


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.


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This resource is funded by a National Institute of General Medical Sciences grant: R24GM145962.

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