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

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.

Dynamic microfluidic single-cell screening identifies pheno-tuning compounds to potentiate tuberculosis therapy.

Drug-recalcitrant infections are a leading global-health concern. Bacterial cells benefit from phenotypic variation, which can suggest effective antimicrobial strategies. However, probing phenotypic variation entails spatiotemporal analysis of individual cells that is technically challenging, and hard to integrate into drug discovery.

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.

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm.

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS.

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.

Deciphering a Pharmacophore Network: A Case Study Using BCR-ABL Data.

This paper introduces a general method that can be used to create groups of pharmacophores to support their further in-depth analysis. A BCR-ABL molecular dataset was used to calculate graph edit distances between pharmacophores and led to their organization into a novel pharmacophore network. The application of a graph layout algorithm allowed us to discriminate between the pharmacophores associated with active compounds and those associated with inactive compounds.

hERG toxicity assessment: Useful guidelines for drug design.

All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years.

Structure-activity relationships of serotonin 5-HT receptors ligands: A review.

5-HT receptors are the most recently discovered serotonergic receptors, for which numerous physiological implications in the central and the peripheral nervous systems as well as the endocrine system are described. A current public health challenge is to propose new and more efficient treatments against neuropsychiatric disorders such as epilepsy or Alzheimer's disease. In this context, 5-HT receptors represent an interesting target for the treatment and prevention of those pathologies, as an alternative or in association with other medicines.

The Pharmacophore Network: A Computational Method for Exploring Structure-Activity Relationships from a Large Chemical Data Set.

Historically, structure-activity relationship (SAR) analysis has focused on small sets of molecules, but in recent years, there has been increasing efforts to analyze the growing amount of data stored in public databases like ChEMBL. The pharmacophore network introduced herein is dedicated to the organization of a set of pharmacophores automatically discovered from a large data set of molecules. The network navigation allows to derive essential tasks of a drug discovery process, including the study of the relations between different chemical series, the analysis of the influence of additional chemical features on the compounds' activity, and the identification of diverse binding modes.

Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity.

This article introduces a new type of structural fragment called a geometrical pattern. Such geometrical patterns are defined as molecular graphs that include a labelling of atoms together with constraints on interatomic distances. The discovery of geometrical patterns in a chemical dataset relies on the induction of multiple decision trees combined in random forests.

A Computational Selection of Metabolite Biomarkers Using Emerging Pattern Mining: A Case Study in Human Hepatocellular Carcinoma.

The biomarker development in metabolomics aims at discriminating diseased from normal subjects and at creating a predictive model that can be used to diagnose new subjects. From a case study on human hepatocellular carcinoma (HCC), we studied for the first time the potential usefulness of the emerging patterns (EPs) that come from the data mining domain. When applied to a metabolomics data set labeled with two classes (e.

Conformation and Dynamics of Human Urotensin II and Urotensin Related Peptide in Aqueous Solution.

Conformation and dynamics of the vasoconstrictive peptides human urotensin II (UII) and urotensin related peptide (URP) have been investigated by both unrestrained and enhanced-sampling molecular-dynamics (MD) simulations and NMR spectroscopy. These peptides are natural ligands of the G-protein coupled urotensin II receptor (UTR) and have been linked to mammalian pathophysiology. UII and URP cannot be characterized by a single structure but exist as an equilibrium of two main classes of ring conformations, open and folded, with rapidly interchanging subtypes.

Discovering structural alerts for mutagenicity using stable emerging molecular patterns.

This study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability.

Dual histamine H3R/serotonin 5-HT4R ligands with antiamnesic properties: pharmacophore-based virtual screening and polypharmacology.

In recent years, preclinical and clinical studies have generated considerable interest in the development of histamine H3 receptor (H3R) antagonists as novel treatment for degenerative disorders associated with impaired cholinergic function. To identify novel scaffolds for H3R antagonism, a common feature-based pharmacophore model was developed and used to screen the 17,194 compounds of the CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie) chemical library. Out of 268 virtual hits which have been gathered in 34 clusters, we were particularly interested in tricyclic derivatives also exhibiting a potent 5HT4R affinity.

Acute toxicity of 8 antidepressants: what are their modes of action?

Currently, the hazard posed by pharmaceutical residues is a major concern of ecotoxicology. Most of the antidepressants belong to a family named the Cationic Amphipathic Drugs known to have specific interactions with cell membranes. The present study assessed the impact of eight antidepressants belonging to selective serotonin reuptake inhibitors or serotonin norepinephrine reuptake inhibitors by the combination of multi-approaches (in vivo, in vitro, in silico) and gives some insights on the mode of action for these molecules.

Assessment of the genotoxic and carcinogenic potentials of 3-aminothiophene derivatives using in vitro and in silico methodologies.

Thiophene derivatives, a class of compounds widely used in products such as pharmaceuticals, agrochemicals or dyestuffs, represent chemicals of concern. Indeed, the thiophene ring is often considered as a structural moiety that may be involved in toxic effects in humans. We primarily focus on the genotoxic/mutagenic and carcinogenic potentials of the methyl 3-amino-4-methylthiophene-2-carboxylate (1), a precursor of the articaine local anesthetic (4) which falls within the scope of the European REACH (Registration, Evaluation, Authorisation and restriction of CHemicals) legislation.

Automated detection of structural alerts (chemical fragments) in (eco)toxicology.

This mini-review describes the evolution of different algorithms dedicated to the automated discovery of chemical fragments associated to (eco)toxicological endpoints. These structural alerts correspond to one of the most interesting approach of in silico toxicology due to their direct link with specific toxicological mechanisms. A number of expert systems are already available but, since the first work in this field which considered a binomial distribution of chemical fragments between two datasets, new data miners were developed and applied with success in chemoinformatics.

Interpretation of honeybees contact toxicity associated to acetylcholinesterase inhibitors.

The widespread use of different pesticides generates adverse effects on non target organisms like honeybees. Organophosphorous and carbamates kill honeybees through the inactivation of acetylcholinesterase (AChE), thereby interfering with nerve signaling and function. For this class of pesticides, it is fundamental to understand the relationship between their structures and the contact toxicity for honeybees.

Consensus QSAR Related to Global or MOA Models: Application to Acute Toxicity for Fish.

Under REACH legislation, alternative methods (in silico or in vitro) like QSAR (Quantitative Structure-Activity Relationships) models are expected to play a significant role. QSARs are based on the assumption that substances with similar chemical structures may have the same biological activities. However, identification of chemical classes could be problematic because chemicals often exhibit different chemical moieties, thereby confounding efforts to achieve a meaningful classification.

Introduction of jumping fragments in combination with QSARs for the assessment of classification in ecotoxicology.

Starting from a random set of structures taken from the European Chemical Bureau (ECB) Web site, an estimation of the classification by acute category in ecotoxicology was carried out. This estimation was based on two approaches. One approach consists in starting with global quantitative structure-activity relationship (QSAR) equations, analyzing the results and defining an interpretation in terms of overall results and mode of action.

Receptor- and ligand-based study on novel 2,2'-bithienyl derivatives as non-peptidic AANAT inhibitors.

Arylalkylamine N-acetyl transferase (serotonin N-acetyl transferase, AANAT) is a critical enzyme in the light-mediated regulation of melatonin production and circadian rythm. With the objective of discovering new chemical entities with inhibitory potencies against AANAT, a medium-throughput screening campaign was performed on a chemolibrary. We found a class of molecules based on a 2,2'-bithienyl scaffold, and compound 1 emerged as a first hit.

Phe369(7.38) at human 5-HT(7) receptors confers interspecies selectivity to antagonists and partial agonists.

Background And Purpose: Human and rat 5-HT(7) receptors were studied with a particular emphasis on the molecular interactions involved in ligand binding, searching for an explanation to the interspecies selectivity observed for a set of compounds. We performed affinity studies, molecular modelling and site-directed mutagenesis, with special focus on residue Phe(7.38) of the human 5-HT(7) receptor [Cys(7.

Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action.

Three quantitative structure-activity relationship (QSAR) models were evaluated for their power to predict the toxicity of chemicals in two datasets: (1) EPAFHM (US Environmental Protection Agency-Fathead Minnow) and (2) derivatives having a high production volume (HPV), as compiled by the European Chemical Bureau. For all three QSAR models, the quality of the predictions was found to be highly dependent on the mode of action of the chemicals. An analysis of outliers from the three models gives some clues for improving the QSAR models.

Novel antagonists of serotonin-4 receptors: synthesis and biological evaluation of pyrrolothienopyrazines.

Based on the definition of a 5-HT(4) receptor antagonist pharmacophore, a series of pyrrolo[1,2-a]thieno[3,2-e] and pyrrolo[1,2-a]thieno[2,3-e] pyrazine derivatives were designed, prepared, and evaluated to determine the properties necessary for high-affinity binding to 5-HT(4) receptors. The compounds were synthesized by substituting the chlorine atom of the pyrazine ring with various N-alkyl-4-piperidinylmethanolates. They were evaluated in binding assays with [(3)H]GR113808 (1) as the 5-HT(4) receptor radioligand.

Novel aminoethylbiphenyls as 5-HT7 receptor ligands.

The synthesis of a series of aminoethylbiphenyls as novel 5-HT(7) receptor ligands is described. The novel derivatives exhibit high affinity for the 5-HT(7) receptor with selectivity toward 5-HT(1A) receptor.