# Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts

> **NIH NIH R44** · COLLABORATIVE DRUG DISCOVERY, INC. · 2021 · $749,928

## Abstract

PROJECT SUMMARY
Collaborative Drug Discovery, Inc. (CDD) proposes to continue development of a novel approach based on
deep learning neural networks to encode molecules into chemically rich vectors. In Phase 1 we demonstrated
that this representation enables computational models that more accurately predict the chemical properties of
molecules than state-of-the-art models, yet are also far simpler to build because they do not require any expert
decisions or optimization to achieve high performance. In Phase 2 we will exploit this unprecedented simplicity
to develop an intuitive software package that will for the first time enable any chemist or biologist working in
drug discovery to create and run their own predictive models – without relying on specialized cheminformatics
expertise – yet still achieve or exceed the accuracy of the best currently available techniques. Scientists engaged
in drug discovery research from academic laboratories to large pharmaceutical companies rely on
computational QSAR models to predict pharmacologically relevant properties and obviate the need to perform
expensive, time-consuming assays (many of which require animal studies) for every molecule of interest.
Improved models will enable researchers to select lead candidate series more effectively, explore chemical
space around leads to generate novel IP more efficiently, reduce failure rates for compounds advancing
through the drug discovery pipeline, and accelerate the entire drug discovery process. These benefits will be
realized broadly across most therapeutic areas.
 We also plan to take the technology one step further, leveraging our chemically rich vector representation to
enable the software to creatively suggest novel compounds (which do not appear in the training libraries,
screening libraries, or lead series) that outperform the lead candidates simultaneously on bioactivity,
ADME/Tox and PK assays . Solving this inverse problem is the Holy Grail of computational medicinal
chemistry and has the potential to revolutionize drug discovery.
!

## Key facts

- **NIH application ID:** 10133177
- **Project number:** 5R44TR002527-03
- **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:** $749,928
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133177

## Citation

> US National Institutes of Health, RePORTER application 10133177, Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts (5R44TR002527-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10133177. Licensed CC0.

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