# 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 · $116,389

## Abstract

Project Summary
The success of the ongoing battle with coronavirus disease 2019 (COVID-19) caused by severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) depends crucially on the availability of
effective diagnostics, vaccines, antibody therapeutics, and small-molecular drugs. Although
SARS-CoV-2 mutates slower than the viruses that cause the flu and the common cold, it has had
more than 8300 observed single mutations on its genome of 29,900 nucleotides by June 1, 2020.
We show that these mutations might have devastating effects on COVID-19 diagnostics,
vaccines, antibody therapeutics, and small-molecular drugs (J. Chem. Inf. Model. In press). We
will develop new artificial intelligence (AI) to forecast SARS-CoV-2 future mutations. Leveraging
on state-of-art methods developed under the present R01 award, we will design mutation-
resistant vaccines, antibody therapeutics, and small-molecular drugs. The CPUs and GPUs
requested in this supplement will be essential for my lab to continue the research of the present
R01 award and to apply the methods developed in this award to attack fundamental problems in
combating COVID-19.

## Key facts

- **NIH application ID:** 10189006
- **Project number:** 3R01GM126189-03S1
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Guowei Wei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $116,389
- **Award type:** 3
- **Project period:** 2018-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189006

## Citation

> US National Institutes of Health, RePORTER application 10189006, Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts (3R01GM126189-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10189006. Licensed CC0.

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