# Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug design

> **NIH NIH R01** · UNIVERSITY OF COLORADO · 2022 · $10,780

## Abstract

PROJECT SUMMARY/ABSTRACT
This Project Summary is unchanged from the original R01. The study of biomolecular interactions and design
of new therapeutics requires accurate physical models of the atomistic interactions between small molecules and
biological macromolecules. Over the least few decades, molecular mechanics force fields have demonstrated the
potential that physical models hold for quantitative biophysical modeling and predictive molecular design. However,
a significant technology gap exists in our ability to build force fields that achieve high accuracy, can be systemati-
cally improved in a statistically robust manner, be extended to new areas of chemistry, can model post-translational
and covalent modifications, are able to quantify systematic errors in predictions, and can be broadly applied across
a high-performance software packages. In this project, we aim to bridge this technology gap to enable new gen-
erations of accurate quantitative biomolecular modeling and (bio)molecular design for chemical biology and drug
discovery. In Aim 1, we will produce a modern, open infrastructure to enable practitioners to rapidly and conve-
niently construct and employ accurate and statistically robust physical force fields via automated machine learning
methods. In Aim 2, we will construct open, machine-readable experimental and quantum chemical datasets that
will accelerate next-generation force field development. In Aim 3, we will develop statistically robust Bayesian
inference techniques to enable the auto- mated construction of type assignment schemes that avoid overfitting
and selection of physical functional forms statistically justified by the data. This approach will also provide an
estimate of the systematic error in predicted properties arising from uncertainty in parameters or functional form
choices—generally the dominant source of error—to be quantified with little added expense. In Aim 4, we will
integrate and apply this infrastructure to produce open, transferable, self-consistent force fields that achieve high
accuracy and broad coverage for modeling small molecule interactions with biomolecules (including unnatural
amino or nucleic acids and covalent modifications by organic molecules), with the ultimate goal of covering all
major biomolecules.
This research is significant in that the technology developed in this project has the potential to radically transform
the study of biomolecular phenomena by providing highly accurate force fields with exceptionally broad chemical
coverage via fully consistent parameterization of organic (bio)molecules. In addition, we will produce new tools to
automate force field creation and tailoring to specific problem domains, quantify the systematic error in predictions,
and identify new data for improving force field accuracy. This will greatly improve our ability to study diverse
biophysical processes at the molecular level, and to rationally design new small-molecule, protein, and nucleic
acid therapeutics. Thi...

## Key facts

- **NIH application ID:** 10592758
- **Project number:** 3R01GM132386-03S1
- **Recipient organization:** UNIVERSITY OF COLORADO
- **Principal Investigator:** Michael R Shirts
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $10,780
- **Award type:** 3
- **Project period:** 2020-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10592758

## Citation

> US National Institutes of Health, RePORTER application 10592758, Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug design (3R01GM132386-03S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10592758. Licensed CC0.

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