# Binding free energy predictions for peptide drugs: A novel machine learning-based peptide simulation model for high-throughput computational lead optimization with quantum mechanical accuracy.

> **NIH NIH R43** · QUANTUM SIMULATION TECHNOLOGIES, INC. · 2024 · $272,369

## Abstract

Project Summary/Abstract
Nearly all modern drug discovery efforts begin with a virtual screening campaign, and there are
now several drugs available on the market that were originally identified through computational
efforts. Small molecule drug discovery, in particular, has benefited from the integration of
computational chemistry tools that have become capable of the necessary throughput due to
advances in computational power and algorithmic development. Binding free energy
calculations have enhanced the practice of small molecule lead optimization and have helped
expedite the development of effective drugs to transform the healthcare landscape.
Peptide drugs are widely considered to be a promising, yet currently underutilized, class of
drugs due to their high level of tunability with modern synthesis methods. Their large exposed
surface area relative to small molecule drugs supports an increased level of specificity and the
roughly 80 peptide drugs currently available on the market represent some of the highest
therapeutic indices of known drugs. Peptide drugs even offer the opportunity to address targets
that were previously thought to be undruggable.
Peptide drugs are situated to similarly benefit from computational lead optimization strategies,
but the application of existing tools for accelerating discovery efforts is less established than for
small molecule drugs. The barrier for achieving widespread success in computational peptide
lead optimization is two-fold: the increased size of peptide drugs makes the necessary
conformational sampling more challenging, and the diversity in chemical character among
different peptide drugs makes it difficult to fit a reliable classical simulation model. The former
challenge can be overcome through efficient conformational sampling approaches, while the
latter can be addressed by quantum mechanical calculations that avoid the need for a restrictive
fit to analytical models. Overcoming both of these challenges simultaneously requires an
innovative approach, because the solutions to each of them independently have been, until
recently, mutually exclusive.
The objective of the proposed research is to make computational lead optimization reliable for
peptide drug discovery by developing a machine learning-based peptide simulation model that
simultaneously overcomes both of the aforementioned challenges. We will achieve this by
training a machine learning potential on the basis of quantum mechanical simulation data, which
will be implemented on our QSP Life molecular simulation platform for efficiently calculating
peptide binding free energies.

## Key facts

- **NIH application ID:** 10820949
- **Project number:** 1R43GM152994-01
- **Recipient organization:** QUANTUM SIMULATION TECHNOLOGIES, INC.
- **Principal Investigator:** Alec White
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $272,369
- **Award type:** 1
- **Project period:** 2024-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10820949

## Citation

> US National Institutes of Health, RePORTER application 10820949, Binding free energy predictions for peptide drugs: A novel machine learning-based peptide simulation model for high-throughput computational lead optimization with quantum mechanical accuracy. (1R43GM152994-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10820949. Licensed CC0.

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