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Related Experiment Video

Updated: May 18, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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
PubMed
Summary
This summary is machine-generated.

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.

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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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Last Updated: May 18, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

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.