# A Machine Learning Platform for Adaptive Chemical Screening

> **NIH NIH R01** · MORGRIDGE INSTITUTE FOR RESEARCH, INC. · 2021 · $433,671

## Abstract

Project Summary
High-throughput screening remains the most common method to identify the primary chemical hits in
chemical biology and drug discovery projects. However, large-scale screens are often financially out of
reach for many academic labs. The resulting small screens of small chemical libraries often identify few
confirmed hits, most of which are unsuitable for medicinal chemistry follow-up. There is a critical need
for computational tools that can guide experimental screening at a reasonable cost while exploring far
more chemicals. This proposal will develop a computational platform that can be applied to any target
or phenotypic system to select compounds for experimental screening from virtual chemical libraries
with a billion or more compounds. New machine learning and high-throughput computing methods will
navigate this large chemical space strategically and efficiently, improving the quality of the identified
compounds. The computationally guided screening can ultimately improve the chemical diversity, target
specificity, and stability of the identified active compounds, accelerating and enabling drug discovery
across NIH Institutes.
The long-term goal of this research is to develop technologies that make chemical screening accessible
to more research groups and enable drug discovery in any therapeutic area. Data science techniques
can take advantage of existing public bioactivity data in PubChem to help prioritize chemical screening
for a new target. In this technology development proposal, the researchers will 1) determine a general
initial screening compound set, 2) develop a computationally-guided iterative screening system, and 3)
create and experimentally validate an end-to-end high-throughput computing workflow that directs drug
discovery for any new target. At each step, rigorous comparisons to experimental results and baseline
models will ensure the methods are working as expected and offer capabilities that do not exist in the
current state-of-the-art computational methods. The resulting computational platform will improve the
efficiency of and reduce the barriers to chemical screening, ultimately making screening for new
compounds available to academic research groups in ways and in places where it has previously been
inaccessible.

## Key facts

- **NIH application ID:** 10263256
- **Project number:** 5R01GM135631-02
- **Recipient organization:** MORGRIDGE INSTITUTE FOR RESEARCH, INC.
- **Principal Investigator:** Anthony James Gitter
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $433,671
- **Award type:** 5
- **Project period:** 2020-09-14 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10263256

## Citation

> US National Institutes of Health, RePORTER application 10263256, A Machine Learning Platform for Adaptive Chemical Screening (5R01GM135631-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10263256. Licensed CC0.

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