Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors
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Updated: Sep 2, 2025
Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
Dušan Petrović1, James S Scott2, Michael S Bodnarchuk2
1Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 431 50, Sweden.
Abstract:
ROS1 rearrangements account for 1-2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives. To that end, we turned to the AstraZeneca virtual library, estimated to cover 1015 synthesizable make-on-demand molecules. We used cloud computing-enabled FastROCS technology to search the enumerated 1010 subset of the full virtual space. A small number of specific libraries were prioritized based on the compound properties and a medicinal chemistry assessment and further enumerated with available building blocks. Following the docking evaluation to the ROS1 structure, the most promising hits were synthesized and tested, resulting in the identification of several potent and selective series. The best among them gave a nanomolar ROS1 inhibitor with over 1000-fold selectivity over TrkA and, from the preliminary established SAR, these have the potential to be further optimized. Our prospective study describes how conceptually simple shape-matching approaches can identify potent and selective compounds by searching ultralarge virtual libraries, demonstrating the applicability of such workflows and their importance in early drug discovery.
Insights
Researchers identified potent and selective ROS1 inhibitors for non-small cell lung cancer treatment using shape-matching virtual screening. This approach successfully discovered novel drug candidates with high selectivity over TrkA, advancing early drug discovery.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Oncology
Background:
- ROS1 rearrangements are implicated in 1-2% of non-small cell lung cancer (NSCLC) cases.
- Currently, no targeted therapies specifically designed for ROS1-positive NSCLC are available.
- TrkA serves as a relevant antitarget for selectivity in ROS1 inhibitor development.
Purpose of the Study:
- To identify potent and selective ROS1 inhibitors through virtual screening.
- To leverage shape-matching approaches for hit discovery in ultralarge chemical libraries.
- To develop novel therapeutic candidates for ROS1-driven cancers.
Main Methods:
- Ligand-based virtual screening using FastROCS technology on a 10^10 molecule subset of the AstraZeneca virtual library.
- Prioritization of libraries based on compound properties and medicinal chemistry assessment.
- Synthesis and biological evaluation of prioritized compounds, including docking to the ROS1 structure.
Main Results:
- Identification of several potent and selective ROS1 inhibitor series.
- Discovery of a lead compound with nanomolar potency against ROS1.
- Achieved over 1000-fold selectivity for ROS1 inhibition compared to TrkA.
- Preliminary structure-activity relationship (SAR) studies indicate optimization potential.
Conclusions:
- Conceptually simple shape-matching methods are effective for identifying potent and selective compounds from vast virtual libraries.
- This workflow demonstrates the utility and importance of virtual screening in early-stage drug discovery for targeted therapies.
- The identified ROS1 inhibitors represent promising leads for further development in NSCLC treatment.
