# Reproducible, Unbiased Ligand Identification Assisted by Artificial Intelligence and Development of Ligand Reference Libraries

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2021 · $561,173

## Abstract

Our current understanding of the molecular mechanisms of disease and structure-based design
of drugs for treatment, rely on experimentally determined 3D structures of proteins and other
macromolecules complexed with small molecule ligands. Many of these structures have direct
relevance to public health, especially complexes of drug targets with drugs, inhibitors, substrates,
or allosteric effectors. Yet, structure-based drug discovery is severely complicated and hindered
by experimental bias and the shortcomings of current methods of experimental ligand
identification, which often result in misidentified, missing, or misplaced ligands. The propagation
of erroneous structures combined with an increased accessibility to structural data not only
thwarts reproducibility in biomedical research and drug discovery, but also diverts valuable
resources down doomed research avenues. We will leverage our extensive experience validating
and refining ligand binding sites to generate ligand reference libraries that will be made publically
available on a new web resource dedicated to the interaction of small molecules and
macromolecules. These libraries can be used in many downstream applications, such as drug
design, computational chemistry, biology, and bioinformatics. We will utilize recent
technological advances in machine learning in conjunction with existing tools to create a
standardized protocol for density interpretation and unbiased, reproducible ligand
identification. This pipeline will not only be able identify and model ligands in unassigned density
fragments, but also be able to detect and correct suboptimally refined ligands in existing
structures. As the proposed AI will be free from cognitive bias, it should alleviate the most severe
problems in structure-based drug design. Because improperly interpreted structures can have a
significant deleterious ripple effect, we will experimentally verify select biomedically important
structures with dubious experimental support for critical small molecules using use X-ray
crystallography or electron microscopy.

## Key facts

- **NIH application ID:** 10200091
- **Project number:** 5R01GM132595-03
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** WLADEK MINOR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $561,173
- **Award type:** 5
- **Project period:** 2019-09-17 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200091

## Citation

> US National Institutes of Health, RePORTER application 10200091, Reproducible, Unbiased Ligand Identification Assisted by Artificial Intelligence and Development of Ligand Reference Libraries (5R01GM132595-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10200091. Licensed CC0.

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