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.

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NEWS & EVENTS
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Getting Started

Beginner guides for molecular modeling, docking, and virtual screening.

Getting Started

The Meeko documentation offers guidance on docking preparation for AutoDock-GPU and AutoDock Vina. The documentation provides instructions for both command-line usage and Python scripting.

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Current Projects

New updates and ongoing projects on AutoDock at RSD3

Current Projects

For an overview of recent work and current services at RSD3,

See Here

Legacy Programs

Access the legacy AutoDock programs and the peripheral tools built around them.

Legacy Programs

For more information about AutoDock4.2.6 (last revision: July 2014), AutoDock Vina v1.1.2 (last revision: May 2011) and the peripheral tools built around them. These sites remain open for information purposes,

See Here

Start Docking with a Current Docking Engine

 AutoDock-GPU

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

 AutoDock-Vina

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

 AutoDockFR

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

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

Learn With Our

Cookbook & Documentations

For Beginners

No specified environment or computing resource required. Run this customizable basic docking example with free resources in a browser.

Open in Colab!

For Teachers

Teach or learn with the practice examples of basic docking, reactive docking and covalent docking with AutoDock engines.

Go to Tutorials!


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.

Copyright © 2024 RSCD3: Resource for Structure-based Computational Drug Discovery and Design