National Resource for
Structure-based Drug Discovery and Design (RSD3)
Computational docking and other structure-based methods are essential tools in drug discovery and design efforts, both in academia and industry.
Building on several decades of work with the AutoDock Suite, the Resource for Structure-based Computational Drug Discovery and Design (RSCD3) will 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. The Resource will streamline access to this toolset by reducing the overhead required to install, learn, and use the software.
The AutoDock Suite is the core of the RSD3 environment. Here, AutoDock has been used to predict binding poses and energies for 300,000 ligands bound to the cancer target ABL kinase, and the best candidates are being chosen interactively using the analysis tool Raccoon.
Overview of the general organization of the RSD3 toolkit under development. Functionalities of tools are exposed by the Python wrapper, extending their functionalities and facilitating the design of user interfaces. The result is a highly modular environment that will allow you to run tools as standalone or combine them to build complex workflows that can be easily deployed using an efficient and modern container technology.
Funding for the resource is provided through a grant from the National Institute of General Medical Sciences (R24GM145962).