# Resource for Structure-based Computational Drug Discovery and Design (RSD3)

> **NIH NIH R24** · SCRIPPS RESEARCH INSTITUTE, THE · 2023 · $1,023,253

## Abstract

Project summary
Automated docking is a computational method that is now routinely used for identifying small molecules that
have the potential to be new drugs. Over the past three decades we have developed docking methods and
our docking software AutoDock is not the most cited docking software with over 40,000 citations in the peer
reviewed literature. As such, it has become an important resource for the community. We seek to create a
national resource that will allow us to maintain and modernize this software to adapt to evolving hardware
platforms and operating systems, to keep the software up to date and relevant by incorporating the latest
algorithmic developments, and to support its large user community. In parallel with these efforts, an important
goal of the proposed Resource is to chart a path toward a self-sustained software ecosystem where
contributors from around the world will contribute to maintain and further develop this software after the
lifetime of this award. This goal has been achieved by other successful open source projects such as Debian
and Python, and the written interest of many colleagues bolster our confidence that the AutoDock software too
can reach this goal. To this end we propose three specific aims. Our first aim is to maintain and modernize the
software code. This critical work is needed for the software to remain functional and able to address the
evolving needs of the community. As we overhaul the software, we will leverage newer toolkits for generating
modern and intuitive graphical user interfaces. These interfaces enable researchers, such as clinical
physicians or chemists with limited computational skills for instance, to use docking to better understand the
mechanism of action of a drug and optimize it. Our second aim is concerned with making our software tools
interoperable with other important modeling software tools such as molecular dynamics for instance. Not only
does this augment the potential of docking to lead to novel therapeutic molecules, but it also serves our goal
to create a community of developers who will ultimately maintain this software ecosystem. Finally our third
and final aim is about supporting the large user community, growing it and ensuring that the software is easily
discovered, obtained and installed.
We have a long track record of developing open source software promoting best practices in software
engineering, and making these tools usable, useful, and available to the community. Likewise we have
supported and grown our user community for many years. This puts us in a unique position to create the
proposed research which, if funded, will allow us to convert this valuable software code into a community
supported software ecosystem ensuring that this software will continue to support the design of novel
therapeutic molecules beyond the lifetime of this award.

## Key facts

- **NIH application ID:** 10707044
- **Project number:** 5R24GM145962-02
- **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE
- **Principal Investigator:** Stefano Forli
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,023,253
- **Award type:** 5
- **Project period:** 2022-09-21 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10707044

## Citation

> US National Institutes of Health, RePORTER application 10707044, Resource for Structure-based Computational Drug Discovery and Design (RSD3) (5R24GM145962-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10707044. Licensed CC0.

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