# Scoring ligand/protein interactions using a novel high performance implementation of DFT quantum mechanics

> **NIH NIH R43** · QUANTUM SIMULATION TECHNOLOGIES, INC. · 2021 · $147,454

## Abstract

Project Summary
In the past several years, dramatic advances in available compute resources have finally enabled computational
chemistry to significantly impact hit-to-lead discovery. Most significantly, free energy perturbation (FEP) calculations
have demonstrated high value for rank-ordering the binding of multiple ligands to a common protein receptor, thereby
allowing acceptably accurate, cost-effective predictions with reasonable turnaround time. Use of this type of approach
has been credited for speeding the hit-to-lead process by a factor of more. (E.g. the computationally-driven Nimbus /
Schrodinger collaboration that yielded an Acetyl-CoA carboxylase inhibitor clinical candidate in 18 months and that was
sold to Gilead in a deal worth as much as $1.2 billion). But, while approaches like FEP are demonstrably useful, they are
limited in application to certain types of systems. This is because they rely on the use of a classical energy function,
which attempts to mimic quantum effects with an approximate form that needs to be parameterized. Ideally, one could,
instead, simply use quantum mechanics (QM), and avoid the significant limitations (in domain of applicability and
accuracy) that arise from using a fitted force field. However, until very recently, high accuracy quantum mechanics
calculations could not be performed on full ligand/protein systems.
We have now demonstrated, for the first time, how a unique distributed memory implementation of high accuracy
quantum mechanics (density functional theory (DFT)) allows ligand/protein binding to be evaluated entirely in the
quantum domain. These calculations can be carried out on commodity hardware (Cloud computers), can finish in less
than an hour and at a cost of less than roughly $10. Having removed the limitations of the classical force field, these
quantum calculations promise to be more broadly applicable, faster, and cheaper than the FEP equivalents. In this grant,
we describe a set of efforts that are required to more fully characterize and validate this approach, and to move from
the potential energies that are directly determined in quantum mechanics to free energy estimates that are more
directly comparable to experimental measurements. We will utilize a benchmark set of consistently-measured
experimental binding data for ligand/protein interactions for a variety of proteins to test and improve our DFT method.
Part of our work will focus on identifying the optimal approach for very large protein systems. While the entire
ligand/protein complex can be treated quantum mechanically with our approach for proteins of size <~ 3000 atoms, for
larger systems, we will need to identify a hybrid approach that treats a large radius around the binding site using QM,
with the remainder of the system treated using a computationally less expensive approach. We also will look to identify
corrections to the QM calculated energies that form a bridge between the fully QM potential energy and free energ...

## Key facts

- **NIH application ID:** 10139861
- **Project number:** 1R43GM140578-01
- **Recipient organization:** QUANTUM SIMULATION TECHNOLOGIES, INC.
- **Principal Investigator:** David A Pearlman
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $147,454
- **Award type:** 1
- **Project period:** 2021-03-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10139861

## Citation

> US National Institutes of Health, RePORTER application 10139861, Scoring ligand/protein interactions using a novel high performance implementation of DFT quantum mechanics (1R43GM140578-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10139861. Licensed CC0.

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