# Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts

> **NIH NIH R01** · MICHIGAN STATE UNIVERSITY · 2020 · $318,777

## Abstract

Project Summary
Fundamental challenges that hinder the current understanding of biomolecular systems are their
tremendous complexity, high dimensionality and excessively large data sets associated with their
geometric modeling and simulations. These challenges call for innovative strategies for handling
massive biomolecular datasets. Topology, in contrast to geometry, provides a unique tool for
dimensionality reduction and data simplification. However, traditional topology typically incurs with
excessive reduction in geometric information. Persistent homology is a new branch of topology
that is able to bridge traditional topology and geometry, but suffers from neglecting biological
information. Built upon PI’s recent work in the topological data analysis of biomolecules, this
project will explore how to integrate topological data analysis and machine learning to significantly
improve the current state-of-the-art predictions of protein-ligand binding and mutation impact
established in the PI’s preliminary studies. These improvements will be achieved through
developing physics-embedded topological methodologies and advanced deep learning
architectures for tackling heterogeneous biomolecular data sets arising from a variety of physical
and biological considerations. Finally, the PI will establish robust databases and online servers
for the proposed predictions.

## Key facts

- **NIH application ID:** 9989158
- **Project number:** 5R01GM126189-03
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Guowei Wei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $318,777
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989158

## Citation

> US National Institutes of Health, RePORTER application 9989158, Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts (5R01GM126189-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9989158. Licensed CC0.

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