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Updated: May 21, 2025
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Spatial Separation of Molecular Conformers and Clusters
Published on: January 9, 2014
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A Molecular Representation to Identify Isofunctional Molecules
Philippe Pinel1,2,3,4, Gwenn Guichaoua1,2,3, Nicolas Devaux4
1Center for Computational Biology, Mines Paris-PSL, PSL Research University, Paris, France.
Molecular Informatics
|March 18, 2025
Summary
Scaffold hopping in drug discovery is challenging. We introduce Interaction Fingerprints Profile (IFPP) for identifying similar molecules, enhanced by Metric Learning for efficient screening of large libraries.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Drug discovery
Background:
- Scaffold hopping is crucial for optimizing drug candidates but faces challenges with protein structure limitations.
- Traditional docking and ligand-based methods struggle with large scaffold hops, especially for membrane proteins.
Purpose of the Study:
- To develop a novel molecular representation, Interaction Fingerprints Profile (IFPP), for identifying isofunctional molecules.
- To enhance the scalability of IFPP for large-scale virtual screening using Metric Learning.
Main Methods:
- IFPP captures molecular binding modes through docking against diverse protein structures.
- Metric Learning approaches were employed to accelerate IFPP similarity estimations.
Main Results:
- IFPP effectively identifies isofunctional molecules, as shown by evaluation on the LH benchmark.
- Metric Learning significantly improves the computational efficiency of IFPP for large libraries.
Conclusions:
- IFPP is a valuable tool for addressing scaffold hopping challenges in drug discovery.
- The integration of Metric Learning makes IFPP a scalable and efficient pre-screening strategy.
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