# Research and cloud deployment of enhanced sampling methods in MovableType

> **NIH NIH R44** · QUANTUMBIO, INC. · 2024 · $654,406

## Abstract

Abstract
The study of protein/ligand binding is one of the central problems in computational biology because of its
importance in understanding intermolecular interactions, and because of its practical payoff in drug discovery
efforts. The transformative impact accurate target/ligand structure can have in the design of next generation
medicines cannot be overstated. If we could routinely and accurately design molecules using these approaches
it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort
and scrutiny on highly active compounds.
In this proposal we describe a novel method we call MovableType (MT) that for the first time will be coupled with
cutting edge enhanced molecular dynamics (MD) methods (e.g., Simulated Tempering, Accelerated MD,
Metadynamics, and Replica exchange MD) in Aims I.1 and II.1a, linear scaling quantum mechanics (for
improved electrostatics) in Aim I.2, and a new Monte Carlo sampling regime called Consecutive Histograms
Monte Carlo (CHMC) in Aim II.1b for increased speed. We expect this development to significantly expand the
domain applicability of MT in particular (and free energy methods in general) to include those situations which
require greater conformational sampling than can be provided by docking alone.
MT addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined
with a new way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the
necessary partition functions, with MT we directly obtain absolute binding free energies and the low free energy
poses (versus most conventional free energy methods in commercial/industrial labs which usually obtain relative
binding free energies). Conceptually, the MT method is analogous to block and type set printing, which allows
us to efficiently evaluate partition functions describing regions or systems of interest. Overall, the MT method is
a general one and can use a broad range of two-body potential functions and can be extended to higher-order
interactions if so desired. Recent work with the MT method has led to the launch of three core product modules:
MTScore (both end state and ensemble-based binding affinity prediction), MTDock (ligand placement), and MTCS
(ligand conformational search). In this project, we will extend our MT product line by optimizing the method for
use with advanced sampling techniques and deliver this methodology to computational chemists for use in their
industrial structure-based drug design campaigns. This work will involve development of a new, integrated tool
for automated structure/model preparation, integration with and optimization for several molecular dynamics
engines, addition an updated electrostatics engine (built on our mature, linear scaling, semi-empirical quantum
mechanics infrastructure), development of a new Monte Carlo method for increased speed, and cloud-based
deployment on the GridMarkets pl...

## Key facts

- **NIH application ID:** 10839514
- **Project number:** 4R44GM148103-02
- **Recipient organization:** QUANTUMBIO, INC.
- **Principal Investigator:** Lance M Westerhoff
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $654,406
- **Award type:** 4N
- **Project period:** 2023-06-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10839514

## Citation

> US National Institutes of Health, RePORTER application 10839514, Research and cloud deployment of enhanced sampling methods in MovableType (4R44GM148103-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10839514. Licensed CC0.

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