Towards a partial order graph for interactive pharmacophore exploration: extraction of pharmacophores activity delta.

This paper presents a novel approach called Pharmacophore Activity Delta for extracting outstanding pharmacophores from a chemogenomic dataset, with a specific focus on a kinase target known as BCR-ABL. The method involves constructing a Hasse diagram, referred to as the pharmacophore network, by utilizing the subgraph partial order as an initial step, leading to the identification of pharmacophores for further evaluation. A pharmacophore is classified as a 'Pharmacophore Activity Delta' if its capability to effectively discriminate between active vs inactive molecules significantly deviates (by at least δ standard deviations) from the mean capability of its related pharmacophores. Among the 1479 molecules associated to BCR-ABL binding data, 130 Pharmacophore Activity Delta were identified. The pharmacophore network reveals distinct regions associated with active and inactive molecules. The study includes a discussion on representative key areas linked to different pharmacophores, emphasizing structure-activity relationships.

PepTraq: a toolbox for in silico data mining and fast sequence filtering.

The development of de novo sequencing tools has led to the massive production of genomes and transcriptomes from many unconventional animal models. To tackle this huge flow of data, PepTraq brings together many functionalities generally scattered in multiple tools, so that sequences can be filtered on the basis of multiple criteria. It is particularly suitable for the identification of non-annotated transcripts, re-annotation, extraction of secretomes, neuropeptidomes, targeted search for peptides and proteins, preparing specific proteomics/peptidomics fasta files for mass spectrometry (MS) applications, MS data processing, etc. PepTraq is developed in Java, and is available as a desktop application that can be downloaded from https://peptraq.greyc.fr . It is also available as a web application at the same URL for processing small files (10-20 MB). The source code is open under a CeCILL-B licence.

New Pharmacophore Fingerprints and Weight-matrix Learning for Virtual Screening. Application to Bcr-Abl Data.

In this work, we propose to analyze the potential of a new type of pharmacophoric descriptors coupled to a novel feature transformation technique, called Weight-Matrix Learning (WML, based on a feed-forward neural network). The application concerns virtual screening on a tyrosine kinase named BCR-ABL. First, the compounds were described using three different families of descriptors: our new pharmacophoric descriptors, and two circular fingerprints, ECFP4 and FCFP4. Afterwards, each of these original molecular representations were transformed using either an unsupervised WML method or a supervised one. Finally, using these transformed representations, K-Means clustering algorithm was applied to automatically partition the molecules. Combining our pharmacophoric descriptors with supervised Weight-Matrix Learning (SWMLR ) leads to clearly superior results in terms of several quality measures.