# Designing selective kinase inhibitors via deep learning

> **NIH NIH R01** · RESEARCH INST OF FOX CHASE CAN CTR · 2022 · $549,557

## Abstract

PROJECT SUMMARY/ABSTRACT
 Modern cancer biology leans heavily on kinase inhibitors as a means to probe the consequences of
deactivating a particular kinase, but the majority of commonly-used chemical probes are not sufficiently target-
selective for robust interpretation of the observed phenotypes. By assembling large panels of kinases
(corresponding to much of the human kinome), it has become possible to determine the selectivity for a given
probe: however, these experiments are expensive and impractical to perform at high throughput. We have
recently developed a new computational approach for rapidly and accurately building 3D structural models of
individual inhibitor/kinase complexes. Aim 1 of this project entails applying deep learning to build models for
predicting the binding affinity of individual inhibitor/kinase pairs, using 3D structural descriptors derived from
the corresponding inhibitor/kinase complexes. Aim 2 of this project will build very large computational libraries
of novel chemical matter, enriched in compounds with 3D properties that complement kinase binding sites. We
will first use the tools developed in Aim 1 to re-evaluate the selectivity of chemical probes that are widely used
by cell biologists, thus informing on which ones are useful tools and which ones should be deprecated. To
provide a replacement for the outdated chemical probes, we will computationally screen the libraries of Aim 2
for more selective compounds, focusing first on CDK kinases and several kinases that represent therapeutic
vulnerabilities in GIST. We will synthesize the top-scoring computational hits, and characterize them using an
escalation of biochemical assays, proteomic kinome profiling, structural biology, and cellular assays. If
successful, this project will deliver new chemical probes for several hitherto unaddressed (“orphan”) kinases, to
serve as chemical tools and as starting point for drug development. Equally importantly though, completion of
this project will provide a robust and validated approach for designing potent and selective kinase inhibitors, to
be subsequently applied for developing new high-quality probes against each of the 500 human kinases.

## Key facts

- **NIH application ID:** 10366318
- **Project number:** 1R01GM141513-01A1
- **Recipient organization:** RESEARCH INST OF FOX CHASE CAN CTR
- **Principal Investigator:** John Karanicolas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $549,557
- **Award type:** 1
- **Project period:** 2022-02-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10366318

## Citation

> US National Institutes of Health, RePORTER application 10366318, Designing selective kinase inhibitors via deep learning (1R01GM141513-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10366318. Licensed CC0.

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