# Metalloenzyme binding affinity prediction with VM2

> **NIH NIH R44** · VERACHEM, LLC · 2024 · $915,334

## Abstract

Project summary: It is estimated that 40 to 50% of known enzymes can be characterized as metalloenzymes,
while currently only 7% of FDA-approved drugs in the United States target this class of protein. This is despite
the fact that there are many dozens of already identified metalloenzyme targets involved in virtually every
therapeutic area, including anti-inflammatory, antibiotics, antivirals, anticancer drugs, and more. This is in large
part because the already very difficult drug design requirement to maintain/increase the potency of an initial
ligand (drug-like molecule) while improving/maintaining its target selectivity and pharmacokinetic properties,
is made even harder by the complicated and often non-intuitive nature of metal-ligand and metal-protein
interactions. Accurate molecular modeling predictions of metalloenzyme-ligand binding affinities, then, would
be highly impactful in pharmaceutical industry drug research and development programs, because they would
allow R&D scientists to carry out computational experiments drastically reducing the number of expensive and
time-consuming bench experiments required to overcome the difficult metalloenzyme inhibitor design
challenges they face. However, currently available molecular modeling approaches are unable to make
predictions reliable enough to do this. Docking and scoring methods are able to determine, in many cases, the
pose of inhibitors in metalloenzyme active sites, but they cannot correctly rank candidate inhibitors in order of
binding affinity as they lack the required detail in their energy models. Recently, free energy-based methods have
advanced to the point of providing reliable binding affinity predictions for many non-metal protein-ligand series
and can, therefore, help speed ligand discovery efforts for these systems. They cannot provide good binding
affinities for metalloenzyme-ligand systems though, because to-date they are all entirely based on classical
forcefields, which fundamentally limits the accuracy of their descriptions of metal-ligand and metal-protein
interactions. This is due, in part, to lack of inclusion of important polarization and charge transfer effects, but it
is also because the complex electronic structure, which metals often exhibit, is intrinsically quantum mechanical.
This fast-track SBIR proposal will address this by developing a new and unique molecular modeling software
tool called Mzyme-QM-VM2, which will provide reliably accurate binding free energies for metalloenzyme-
inhibitor complexes by a novel combination of statistical mechanics and highly scalable quantum chemistry
methods. This software will be based on mining minima free energy calculation methodology and will be
developed as an extension of VeraChem's VM2 free energy software platform.

## Key facts

- **NIH application ID:** 10931919
- **Project number:** 4R44GM150323-02
- **Recipient organization:** VERACHEM, LLC
- **Principal Investigator:** Simon Webb
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $915,334
- **Award type:** 4N
- **Project period:** 2023-05-01 → 2025-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931919

## Citation

> US National Institutes of Health, RePORTER application 10931919, Metalloenzyme binding affinity prediction with VM2 (4R44GM150323-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10931919. Licensed CC0.

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