Damien Geslin

In Molecular Informatics

Abstract

Abstract The exploration of chemical space is a fundamental aspect of chemoinformatics, particularly when one explores a large compound data set to relate chemical structures with molecular properties. In this study, we extend our previous work on chemical space visualization at the pharmacophoric level. Instead of using conventional binary classification of affinity (active vs inactive), we introduce a refined approach that categorizes compounds into four distinct classes based on their activity levels: super active, very active, active, and inactive. This classification enriches the color scheme applied to pharmacophore space, where the color representation of a pharmacophore hypothesis is driven by the associated compounds. Using the BCR-ABL tyrosine kinase as a case study, we identified intriguing regions corresponding to pharmacophore activity discontinuities, providing valuable insights for structure-activity relationships analysis.

Refinement of a ligand activity and representation of topological phamacophores in a colored network

By Maroua Lejmi, Damien Geslin, Bertrand Cuissart, Ilef Ben Slima, Nida Meddouri, Ronan Bureau, Alban Lepailleur, Amel Borgi, Jean-Luc Lamotte

2023-10-01

In Proceedings of the 11èmes journées de la société française de chémoinformatique

Abstract

Structure-Activity Relationships is a critical aspect of drug design. It enables us to examine ligand interactions and performances towards specific targets, then to design effective drugs for treating diseases or improving existing medical therapies. In this context, we specifically study the activity of ligands towards kinases using the BCR-ABL dataset. The work is dedicated to introduce a refinement method for the activity of molecules. Instead of considering anity as a binary activity, a molecule being either active or inactive, the compounds were partitioned into 4 classes according to their activity: very active, moderately active, slightly active, inactive. This activity is later used to evaluate molecular descriptors called topological pharmacophores [1]. These pharmacophores provide essential information by representing the key structural features of a molecule. Their quality is determined by measuring their “growth-rate” which corresponds to the ratio of active molecules over inactive ones, among the molecules supported by the pharmacophore. In our work, the calculation of the growth-rate is based on the classes of activity that we have created. Consequently, we will obtain three measurements of the growth rate, each one being related to a class of activity. In addition, we proposed to convert the new information of the quality of the pharmacophores into a visual representation called “The Pharmacophore Network” [2]. The latter is a graph whose nodes represent the pharmacophores and edges represent a graph-edit distance that separates them. Our goal was to structure more finely the pharmacophore space and to be able to detect visually interesting areas that can be explored. For this purpose, we integrated colors in this Pharmacophore Network, where each color refers to a class of activity.

Navigating pharmacophore space to identify activity discontinuities: A case study with BCR-ABL

Abstract

Refinement of a ligand activity and representation of topological phamacophores in a colored network

Abstract

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