# Next generation free energy perturbation (FEP) calculations--enabled by a novel integration of quantum mechanics (QM) with molecular dynamics allowing a large QM region and no sampling compromises

> **NIH NIH R44** · QUANTUM SIMULATION TECHNOLOGIES, INC. · 2024 · $1,022,908

## Abstract

Project Summary
Computational chemistry has revolutionized drug discovery, reducing by months or even years the amount of
time it takes to discover and refine a lead candidate. While computational chemistry has impacted discovery in
many ways, the greatest impact has been via virtual screening (to identify potential hits) and, more recently,
through free energy calculations. Free energy calculations such as free energy perturbation (FEP) are used in
hit-to-lead improvement. This discovery phase is typically carried out using costly bench chemistry, and tools
that can help reduce the number of potential modifications that can appreciably improve efficiency and reduce
cost. FEP allows one to evaluate the relative binding of a series of similar ligands to a receptor (protein or
nucleic acid) and focus only on those predicted to provide the greatest improvement.
While FEP has demonstrated multiple successes in terms of accelerating drug discovery, and while FEP is
now integrated as part of drug discovery at most pharmaceutical companies, it suffers from one fundamental
weakness, as commonly practiced: FEP is built upon a so-called classical molecular mechanical (MM) energy
function. This is a relatively simple function that crudely approximates the real-world energetics. The true
energetics are only properly represented using the equations of quantum mechanics (QM). But these
equations are so complex, and so computationally difficult to solve, that the entirety of the pharma-relevant
toolbox has traditionally integrated the inferior MM approach. FEP has been no different.
The result is that while FEP is quite accurate for certain systems where the MM approximations work well,
there are a large number of other systems—many of them quite important to drug discovery—for which MM,
and MM-based FEP are not reliably predictive. Since discovery projects at pharmaceutical companies are
chosen on the basis of biology and not on the basis of their suitability for computational chemistry, it is critically
important to identify an approach that can broaden the scope of usefulness for FEP to accommodate more of
the targets of interest to pharma. But replacing MM with QM in computational tools has, until our work, led to
methods incapable of pharma-relevant turnaround (a few days or less).
We have broken this barrier using a hybrid approach QM/MM approach, which provides significantly broader
and more accurate coverage of the entirety of the system space of interest to pharma while keeping
computational costs and turnaround low enough to be attractive to pharma. Our platform, QUELO, is unique
and demonstrably useful. In the first phase of this grant, we implemented, validated, and began marketing it.
In this grant phase, we propose algorithmic changes to QUELO to make it more computationally efficient and
modifications to allow it to be run on GPU (rather than CPU) computing platforms, which will further reduce
total cost and improve turnaround. This will accelera...

## Key facts

- **NIH application ID:** 11007917
- **Project number:** 2R44GM150314-02
- **Recipient organization:** QUANTUM SIMULATION TECHNOLOGIES, INC.
- **Principal Investigator:** David A Pearlman
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,022,908
- **Award type:** 2
- **Project period:** 2023-04-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11007917

## Citation

> US National Institutes of Health, RePORTER application 11007917, Next generation free energy perturbation (FEP) calculations--enabled by a novel integration of quantum mechanics (QM) with molecular dynamics allowing a large QM region and no sampling compromises (2R44GM150314-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/11007917. Licensed CC0.

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