# Intelligent Chemical Structure Browser for Drug Discovery and Optimization

> **NIH NIH R44** · COLLABORATIVE DRUG DISCOVERY, INC. · 2021 · $727,255

## Abstract

PROJECT SUMMARY
Collaborative Drug Discovery, Inc. (CDD) proposes to develop a novel intelligent data browser that will enable
medicinal chemists developing new drug compounds to more efficiently browse and organize experimental
data in an intuitive way. The proposed browser will essentially “hyperlink” chemical space and allow chemists
to navigate easily among compounds in a chemical lead series following the same pathways that lead from one
compound to the next in the mental models that they intuitively map in their heads. Navigating through and
extending a lead series to discover the optimal drug candidate to advance into animal studies and clinical trials
comprises a critical stage of the drug discovery pipeline: the success of large subsequent investments depends
on making the right decision. This stage also especially emphasizes creative and intuitive thinking. Existing
software that assists scientists engaged in this task tabulates data in formats that make it difficult to assemble
and compare the essential data needed to rapidly explore ideas about how to further optimize promising
candidates. Our proposed intelligent browser will support more natural and intuitive workflows.
 A key enabling innovation for this technology is a methodology that we have developed to organize
molecular structures through a partial ordering based on the substructure-superstructure relation as a Hasse
diagram. Our semilattice representation provides a machine computable format that can capture the
relationships among related chemical entities that a medicinal chemist intuits.
 Expected key impacts include (1) faster development of lead series into drug candidates, (2) cost savings due
to more efficient use of synthesis and assay resources, and most importantly (3) better scientific decisions
about which compounds to pursue and advance into the clinical pipeline. Better decisions at this stage in the
drug discovery process should increase the probability that drug candidates that are chosen will successfully
emerge through the clinical pipeline as FDA approved drugs, and improve the effectiveness and safety profile of
those drugs. Even a small increase in these probabilities multiplied by the size of the investments required to
take drugs through clinical trials translates into a large value. We have validated this perception of value in
preliminary market research with potential pharmaceutical company customers.
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## Key facts

- **NIH application ID:** 10241834
- **Project number:** 2R44TR002699-02A1
- **Recipient organization:** COLLABORATIVE DRUG DISCOVERY, INC.
- **Principal Investigator:** BARRY A BUNIN
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $727,255
- **Award type:** 2
- **Project period:** 2019-02-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241834

## Citation

> US National Institutes of Health, RePORTER application 10241834, Intelligent Chemical Structure Browser for Drug Discovery and Optimization (2R44TR002699-02A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10241834. Licensed CC0.

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