# Machine learning approaches for the discovery, repurposing, and optimization of natural products with therapeutic potential - Supplement to support grad training of Adrian Russ

> **NIH NIH R35** · VANDERBILT UNIVERSITY · 2024 · $28,314

## Abstract

Project Summary
Natural products from bacteria, fungi, and plants have long been a rich source of molecules with
fascinating chemical structures and therapeutically-relevant bioactivities. One major challenge in
natural product discovery is rediscovery of known compounds. One way to overcome this
challenge is to mine for natural products from underexplored taxa. As part of the parent award,
we applied machine learning and other bioinformatics techniques to identify the taxa that are most
likely to produce multiple novel and bioactive compounds and few or no known active compounds,
increasing the chances of discovering novel active compounds. In the work supported by this
supplement, a graduate student, Adrian Russ, will perform further bioinformatic analysis of these
prioritized strains, isolate active natural products from the strains, and investigate the strains’
response to commonly used elicitors of secondary metabolism.
This work is related to the first project in the parent award, which involves the development
application of machine learning methods for studying structure-activity relationships of natural
products and applying these machine learning methods to discover natural products with
therapeutically relevant bioactivities. Adrian’s project has three aims 1) to apply bioinformatics to
investigate the conservation and diversity of BGCs in the genera of interest to determine which
genera are most likely to harbor unsequenced biosynthetic diversity 2) to isolate and characterize
bioactive natural products from the prioritized strains and 3) to determine if the strains’ secondary
metabolism is stimulated by elicitors that have been successful in Streptomyces and other well-
studied secondary metabolite producers. Through this project, Adrian will learn multiple
computational, microbiology, molecular biology, and analytical chemistry techniques which will
likely be useful in his planned career. This award will also support Adrian’s career development
by funding his travel to conferences and enabling him to dedicate more time to research.

## Key facts

- **NIH application ID:** 10943085
- **Project number:** 3R35GM146987-02S2
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Allison Sara Walker
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $28,314
- **Award type:** 3
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10943085

## Citation

> US National Institutes of Health, RePORTER application 10943085, Machine learning approaches for the discovery, repurposing, and optimization of natural products with therapeutic potential - Supplement to support grad training of Adrian Russ (3R35GM146987-02S2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10943085. Licensed CC0.

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