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Updated: May 18, 2026
08:31
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Algorithm-driven, phenotype-directed bioactive molecular discovery
Amalia-Sofia Piticari1, Samuel D Griggs2,3, Laura Crawford1
1Rosalind Franklin Institute, Harwell Campus, Didcot, Oxfordshire, OX11 0QX, UK.
Communications Chemistry
|May 16, 2026
Summary
This study introduces an automated workflow for phenotype-driven drug discovery, accelerating the identification of novel bioactive molecules with unexpected mechanisms. The system uses algorithms to design, synthesize, and test compounds, leading to new tubulin modulators.
Area of Science:
- Drug Discovery and Medicinal Chemistry
- Chemical Biology
- Automation and Robotics
Background:
- Traditional small molecule discovery relies on iterative cycles targeting specific proteins.
- Phenotype-driven discovery offers a route to novel drugs with unknown mechanisms of action.
Purpose of the Study:
- To develop a fully closed-loop, algorithm-driven workflow for phenotype-driven molecular discovery.
- To demonstrate the workflow's capability in identifying and optimizing bioactive molecules.
Main Methods:
- Construction of a large virtual reaction space from substrate and co-substrate pairs.
- Algorithmic design and automated execution of chemical reactions.
- Phenotypic screening of reaction products and iterative optimization guided by observed hits.
Main Results:
- Successful application of the workflow using Rh-catalysed annulations and the cell painting assay.
- Discovery and structural evolution of novel tubulin modulators.
- Demonstration of the workflow's generality across different chemistries and assay modalities.
Conclusions:
- The presented algorithm-driven workflow accelerates the discovery of chemical probes and potential first-in-class drugs.
- The approach is generalizable for automated, function-directed exploration of chemical space.
- This method opens new avenues for drug discovery by uncovering molecules with unexpected mechanisms.
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