# Computational Infrastructure for Automated Force Field Development and Optimization

> **NIH NIH R44** · ATTMOS INC. · 2024 · $1,146,639

## Abstract

Project Abstract
 Our overarching goal is to provide reliable and efficient tools that can be used in structure
based drug discovery (SBDD). One crucial component of SBDD is to predict the structure of a
drug molecule that binds to a protein involved in a certain disease. This is usually achieved using
computer tools and the process consists of two steps, namely hit identification and lead
optimization. The latter step requires high accuracy and is presently achieved by computing
relative binding free energies (RBFE) using alchemical methods and molecular mechanics (MM)
forcefields. Unfortunately, due to deficiencies in MM forcefields, predicted drug candidates using
the SBDD process are sometimes unreliable, which is only realized at the later stages of the drug
discovery process involving experimental studies or even clinical trials. To address this issue, we
will create a novel, flexible and user-friendly computational infrastructure named Automated Force
Field Developer and Optimizer (AFFDO) that will allow scientists to quickly generate high-quality
training datasets through high-throughput ab initio calculations and transform them into fast and
accurate models which can then be used for RBFE calculations. We will engineer a commercial
quality code and deploy it on an existing web-based, user-friendly, drug development platform
that is widely popular among the industrial community (OpenEye’s Orion platform).

## Key facts

- **NIH application ID:** 10932792
- **Project number:** 4R44GM150347-02
- **Recipient organization:** ATTMOS INC.
- **Principal Investigator:** Madu Manathunga
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,146,639
- **Award type:** 4N
- **Project period:** 2023-09-30 → 2026-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932792

## Citation

> US National Institutes of Health, RePORTER application 10932792, Computational Infrastructure for Automated Force Field Development and Optimization (4R44GM150347-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10932792. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*