# Quantum Chemistry Methods for Rational Drug Design

> **NIH NIH R43** · Q-CHEM, INC. · 2023 · $247,909

## Abstract

Project Summary
A scalable computational quantum mechanics method for non-covalent protein-ligand interactions will be developed
based on "extended" symmetry-adapted perturbation theory (XSAPT), a cubic-scaling, fragment-based approach
that is specifically designed for large supramolecular complexes, and which affords a demonstrated accuracy of
;$ 1 kcal/mo! with respect to the best-available ab initio benchmarks. In Phase I of this work, we will enhance
the efficiency of XSAPT via better parallelization that will enable routine application to protein-ligand models
containing 300+ atoms, using only modest computational resources. A bootstrap procedure will be developed to
assess the accuracy of the method and a data set will be generated that includes protein-ligand interaction energies
and their components: electrostatics, steric repulsion, dispersion, polarization, and charge transfer. The data set
will build upon standard ones derived from crystal structures but will also include nonequilibrium structures as
well as small ligand fragments for which crystal structures and other experimental data are not available; the latter
are representative of fragment-based drug discovery strategies. These are challenging cases for interaction energy
computations that can only be addressed quantitatively by using the predictive power of quantum mechanics, not by
empirical scoring functions or by fits to experimental data. In Phase II, this data set will be used to train a machine
learning (ML) model that is capable of ranking-ordering ligand binding energies in a reliable and quantitative
way, something that existing scoring functions ( even those based on ML) cannot do. Additional Phase II work
will integrate the ML-XSAPT scoring function into virtual drug-discovery workflows (including flexible docking
protocols), which will facilitate both lead generation and lead optimization in drug discovery, based on quantitative
ab initio energetics.

## Key facts

- **NIH application ID:** 10697148
- **Project number:** 1R43GM148095-01A1
- **Recipient organization:** Q-CHEM, INC.
- **Principal Investigator:** Xintian Feng
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $247,909
- **Award type:** 1
- **Project period:** 2023-05-15 → 2024-11-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10697148

## Citation

> US National Institutes of Health, RePORTER application 10697148, Quantum Chemistry Methods for Rational Drug Design (1R43GM148095-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10697148. Licensed CC0.

---

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