# Next-generation alchemical free energy methods and quantum/machine-learning models for drug discovery

> **NIH NIH R01** · RUTGERS, THE STATE UNIV OF N.J. · 2024 · $325,881

## Abstract

Next-generation alchemical free energy methods and quantum/machine-learning models for
 drug discovery.
PI: Darrin M. York, Rutgers University, Piscataway, NJ 08854-8087 USA.
 Alchemical free energy (AFE) simulations are indispensable in various aspects of drug discovery by enabling
the prediction of ligand binding affinity and selectivity. A critical barrier to progress is the current limitation in pre-
cision and accuracy of AFE simulations that restricts their predictive capability. The current proposal addresses
these barriers with new AFE methods and models that will be integrated into the GPU-accelerated AMBER soft-
ware suite used worldwide (over 30K users) in academia, government labs and industry. Specifically, we propose
to: 1. Create advanced technology for robust high-precision AFE simulations; 2. Develop accurate quantum
mechanical/deep-learning potential (QDπ) force fields for drug discovery and 3. Validate precision and accu-
racy of AFE methods and QDπ model. In Aim 1, we will develop new technologies for robust and reproducible
calculation of ligand-protein binding free energies of compound libraries. The methods work together to enable
highly precise, converged AFE simulations across thermodynamic graph networks. In Aim 2, we will develop a
highly accurate and computationally efficient general quantum deep-potential interaction (QDπ) force field model
for drug discovery. The QDπ model will be formulated as a machine learning potential correction (∆-MLP) to the
quantum mechanical/molecular mechanical (QM/MM) energy using fast, approximate 3rd-order density-functional
tight-binding QM model and well-established AMBER MM force fields and compatible water and ions models. The
∆-MLP will leverage our recently developed range-corrected deep-learning potential (DPRc) for accurate intra-
and intermolecular interactions. In Aim 3, we will conduct in depth validation studies of the AFE methods from
Aim 1 and QDπ model of Aim 2 on a systematic set of benchmark systems, including macrophage migration
inhibitory factor (MIF), JAK2 JH2 domain, SARS-Cov2 Mpro, and sigma 1 and 2 receptors.

## Key facts

- **NIH application ID:** 10932888
- **Project number:** 5R01GM107485-10
- **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:** 2024
- **Award amount:** $325,881
- **Award type:** 5
- **Project period:** 2015-08-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932888

## Citation

> US National Institutes of Health, RePORTER application 10932888, Next-generation alchemical free energy methods and quantum/machine-learning models for drug discovery (5R01GM107485-10). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10932888. Licensed CC0.

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