# From atoms to mechanisms - Artificial Intelligence augmented molecular simulations for mechanistic ligand design

> **NIH NIH R35** · UNIV OF MARYLAND, COLLEGE PARK · 2024 · $77,817

## Abstract

While there have been numerous advances in computational methods aiding in rational drug design, most
approaches so far take a static view of the drug target, ignoring the complexities associated with the
dynamics and thermodynamics of conformational transitions. There is thus a pressing need for new
computational methods that are accurate, tractable and automatable for large scale drug discovery and
chemical biology studies that account for the changing nature of a generic drug target. Built on strong
theoretical and computational preliminary results, this program seeks to understand and guide design of
new inhibitors of tyrosine kinases and model riboswitches in a mechanistically guided paradigm.
Our research program is driven by the central hypotheses that (a) mechanistically aware ligand design
strategies can outperform traditional strategies guided only by structure, and (b) artificial intelligence (AI)-
integrated molecular dynamics (MD) simulation methods can help learn mechanisms in a high-throughput
fashion. Our program can be split into two overarching yet complementary thematic areas. In the first area,
we will develop sampling algorithms at the interface of statistical mechanics, MD simulations and AI to
probe mechanisms for rare event processes, such as drug unbinding and conformational change.
Specifically we will build on our preliminary work in using ideas from neural information processing and
natural language processing, and adapt them for advanced sampling methods that learn reaction coordinate,
thermodynamics and kinetics on-the-fly as the simulation progresses. In addition, we will also be interacting
closely with other leading computational groups to integrate our sampling methods with theirs and to
facilitate efficient, accurate sampling for polarizable force-field development. In the second area, we will
use our algorithms to guide mechanistically driven design of inhibitors of Src, Abl kinases and PreQ1
riboswitches. We will take a mechanistically driven perspective wherein we will map out the different
conformations of a given target and understand how an existing ligand interacts with these, and then propose
ligand modifications based upon this understanding. We will use our AI-augmented MD methods to
understand the dissociation mechanisms of ligands in one uninterrupted “atoms to mechanism” workflow
with minimal human intervention. All our predictions will be validated in different ways by our
experimental collaborators at Stony Brook University and the National Cancer Institute

## Key facts

- **NIH application ID:** 11167992
- **Project number:** 3R35GM142719-04S1
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Pratyush Tiwary
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $77,817
- **Award type:** 3
- **Project period:** 2021-09-18 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11167992

## Citation

> US National Institutes of Health, RePORTER application 11167992, From atoms to mechanisms - Artificial Intelligence augmented molecular simulations for mechanistic ligand design (3R35GM142719-04S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11167992. Licensed CC0.

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