# Next-generation integrated quantum force fields for biomedical applications

> **NIH NIH R01** · RUTGERS, THE STATE UNIV OF N.J. · 2021 · $322,545

## Abstract

Next-generation integrated quantum force fields for biomedical applications
PI: Darrin M. York, Rutgers University, Piscataway, NJ 08854-8087 USA.
 We have recently developed novel framework for next-generation quantum mechanical force fields (QMFFs)
designed to meet the challenges of biomolecular simulations and drug discovery applications. QMFFs have
tremendous computational advantages relative to their fully QM counterparts, being inherently parallelizable and
linearly scaling, offering tremendous computational speedup, and promising quantitative accuracy potentially
superior to full QM methods. QMFFs accurately model multipolar electrostatics, charge penetration effects, and
non-linear polarization response. QMFFs thus offer a transformative technology for drug discovery applications, in
particular, for advancing the predictive capability of free energy simulations in lead refinement. These are critically
important for the diverse chemical space of drug molecules, including halogen bonding, cation-  and metal-ligand
interactions. Further, QMFFs offer a mechanism for modeling covalent inhibitors. Specifically, we propose to: I.
Develop new QMFFs for drug discovery. QMFFs will be developed based on both semiempirical and ab initio
density-functional methods in the following stages: 1) determination of multipolar mapping parameters enhancing
the DFTB electrostatic potential to reach greater accuracy, 2) augmentation of electronic response terms using
chemical potential equalization (CPE) corrections using an orthogonal perturbation-response approach to solve
the under-polarization problem of DFTB methods, 3) parameterization of non-electrostatic non-bonded interac-
tion parameters using realistic potentials that capture many-body exchange and dispersion interactions, and 4)
exploration of statistical potentials, using machine learning approaches applied to quantum data sets, to correct
internal conformational energies and short-range interactions. II. Develop new free energy methods to enable
protein-ligand binding predictions using QMFFs. We will develop a novel integrated free energy pipeline to pre-
dict alchemical binding free energies for ligands and inhibitors. This will include new GPU-accelerated methods
for  -space self-adaptive mixture sampling ( -SAMS) and 2D-vFEP analysis, coupled with conformational space
enhanced sampling methods for alchemical steps of the thermodynamic cycle, and advancements in free en-
ergy “book-ending” methods (BBQm) to efficiently connect molecular mechanical force field and QMFF model
representations. III. Test and validate QMFFs and free energy methods, and apply to MIF inhibitor binding. The
methods will be broadly tested against established data sets for solvation free energies, and a drug discovery
data set. More in-depth validation studies will be conducted by examining the relative binding free energies of
inhibitors of the macrophage inhibitory factor (MIF). Finally, exploratory applications will examine m...

## Key facts

- **NIH application ID:** 10202634
- **Project number:** 5R01GM107485-07
- **Recipient organization:** RUTGERS, THE STATE UNIV OF N.J.
- **Principal Investigator:** Darrin M York
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $322,545
- **Award type:** 5
- **Project period:** 2015-08-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10202634

## Citation

> US National Institutes of Health, RePORTER application 10202634, Next-generation integrated quantum force fields for biomedical applications (5R01GM107485-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10202634. Licensed CC0.

---

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