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Related Experiment Video

Updated: Mar 17, 2026

Covalent Fragment Screening Using the Quantitative Irreversible Tethering Assay

Covalent Fragment Screening Using the Quantitative Irreversible Tethering Assay

Published on: February 28, 2025

ISIDA Property-Labelled Fragment Descriptors.

Fiorella Ruggiu¹, Gilles Marcou¹, Alexandre Varnek¹

¹Laboratoire d'Infochimie, UMR 7177 Université de Strasbourg-CNRS, Institut de Chimie, 4, rue Blaise Pascal, 67000 Strasbourg, France phone: +33687934703.

Molecular Informatics

|July 28, 2016

Summary

ISIDA Property-Labelled Fragment Descriptors (IPLF) offer a versatile method for encoding molecular structures. These descriptors, particularly tree-based ones, show promise in virtual screening and quantitative structure-activity relationship (QSAR) modeling.

Keywords:

Electrostatic potential Fragment counts Molecular descriptors Neighbourhood behaviour Pharmacophore features Protease inhibition QSAR Virtual screening hERG logP

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Area of Science:

Chemoinformatics
Computational Chemistry
Molecular Modeling

Background:

Molecular structure encoding is crucial for cheminformatics and drug discovery.
Existing descriptors may not fully capture complex molecular properties, especially under varying conditions like pH.

Purpose of the Study:

To introduce and evaluate ISIDA Property-Labelled Fragment Descriptors (IPLF) as a novel framework for numerical molecular representation.
To explore the utility of IPLFs, particularly pH-dependent variants, in chemoinformatics tasks.

Main Methods:

Developed IPLFs by combining graph fragmentation schemes with property-based atom coloring (e.g., pH-dependent, electrostatic potential).
Implemented fragmentation supporting linear sequences, pairs, circular fragments, and feature trees.
Utilized fuzzy rendering for flexible descriptor generation, with options to include or ignore bond information.

Main Results:

Selected IPLF subsets, especially tree descriptors, demonstrated strong performance in neighborhood analysis and QSAR modeling.
Achieved excellent results in similarity-based virtual screening for protease inhibitors.
Generated highly predictive models for octanol-water partition coefficient and hERG channel inhibition.

Conclusions:

IPLF provides a powerful and flexible approach to molecular descriptor generation.
pH-dependent feature flagging offers advantages over classical descriptors.
IPLF shows significant potential for applications in drug discovery, virtual screening, and predictive modeling.