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.

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.

A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors.

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.

Cryptic Pockets Repository through Pocket Dynamics Tracking and Metadynamics on Essential Dynamics Space: Applications to Mcl-1.

Detection of cryptic pockets (hidden protein pockets) is a hot topic in structure-based drug discovery, especially for drugging the yet undruggable proteome. The experimental detection of cryptic pockets is still considered an expensive endeavor. Thus, computational methods, such as atomistic simulations, are used instead.

Disproportionality analysis in VigiBase as a drug repositioning method for the discovery of potentially useful drugs in Alzheimer's disease.

Unlabelled: Drug repositioning aims to propose new indications for marketed drugs. Although several methods exist, the utility of pharmacovigilance databases for this purpose is unclear. We conducted a disproportionality analysis in the World Health Organization pharmacovigilance database VigiBase to identify potential anticholinesterase drug candidates for repositioning in Alzheimer's disease (AD).

Insights into Mcl-1 Conformational States and Allosteric Inhibition Mechanism from Molecular Dynamics Simulations, Enhanced Sampling, and Pocket Crosstalk Analysis.

In this study, we explored the structural dynamics of Mcl-1, an anti-apoptotic protein. On the basis of structural ensembles, the essential dynamics was extracted and showed two major axes of variability: a breathing motion at the binding interface and a correlated motion through the internal loops. A free energy surface characterizing the breathing motion at the binding interface was generated and suggested an equilibrium between a closed conformation and a "ready to bind" conformation as the predominant states of Mcl-1 in solution.

Drug repositioning: a brief overview.

Objectives: Drug repositioning, that is, the use of a drug in an indication other than the one for which it was initially marketed, is a growing trend. Its origins lie mainly in the attrition experienced in recent years in the field of new drug discovery.

Key Findings: Despite some regulatory and economic challenges, drug repositioning offers many advantages, and a number of recent successes have confirmed both its public health benefits and its commercial value.

Hot-Spots of Mcl-1 Protein.

Protein-protein interactions (PPIs) control many important physiological processes within human cells. Apoptosis or programmed cell death is closely regulated by pro- and antiapoptotic signals. Dysregulation of this homeostasis is implicated in tumorigenesis and acquired resistance to treatments.

Synthesis of 3,3-Diarylazetidines by Calcium(II)-Catalyzed Friedel-Crafts Reaction of Azetidinols with Unexpected Cbz Enhanced Reactivity.

Azetidines are valuable motifs that readily access under explored chemical space for drug discovery. 3,3-Diarylazetidines are prepared in high yield from N-Cbz azetidinols in a calcium(II)-catalyzed Friedel-Crafts alkylation of (hetero)aromatics and phenols, including complex phenols such as β-estradiol. Electron poor phenols undergo O-alkylation.

Structure-guided design of pyridoclax derivatives based on Noxa / Mcl-1 interaction mode.

Protein-protein interactions are attractive targets because they control numerous cellular processes. In oncology, apoptosis regulating Bcl-2 family proteins are of particular interest. Apoptotic cell death is controlled via PPIs between the anti-apoptotic proteins hydrophobic groove and the pro-apoptotic proteins BH3 domain.

Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods.

The assessment of acute toxicity of chemicals by in silico methods is actually done by two methodologies, read-across and QSAR. The two approaches are strongly based on the similarity between the chemical for which a risk assessment is required and the reference chemical(s) for which the experimental data are known. Here, we describe the two methodologies with some main publications as illustrations and the in silico data associated with acute toxicity endpoints (ECHA, REACH) accessible via eChemPortal.

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.

Joint effects of nine antidepressants on Raphidocelis subcapitata and Skeletonema marinoi: A matter of amine functional groups.

Antidepressants are among the most prescribed pharmaceuticals throughout the world. Their presence has already been detected in several aquatic ecosystems worldwide and their effects on non-target organisms justify the growing concern of both the public and regulatory authorities. These emerging pollutants do not occur as isolated compounds but rather as multi-component mixtures, which may lead to increased adverse effects compared to individual compounds.

Toward Understanding Mcl-1 Promiscuous and Specific Binding Mode.

Mcl-1, which is an anti-apoptotic member of the Bcl-2 protein family, is overexpressed in various cancers and promotes the aberrant survival of tumor cells. To inhibit Mcl-1, and initiate apoptosis, an interaction between BH3-only proteins and Mcl-1 anti-apoptotic protein is necessary. These protein-protein interactions exhibit some selectivity: Mcl-1 binds specifically to Noxa, whereas Bim and Puma bind strongly to all anti-apoptotic proteins.

The Arg-Phe-amide peptide 26RFa/glutamine RF-amide peptide and its receptor: IUPHAR Review 24.

The RFamide neuropeptide 26RFa was first isolated from the brain of the European green frog on the basis of cross-reactivity with antibodies raised against bovine neuropeptide FF (NPFF). 26RFa and its N-terminally extended form glutamine RF-amide peptide (QRFP) have been identified as cognate ligands of the former orphan receptor GPR103, now renamed glutamine RF-amide peptide receptor (QRFP receptor). The 26RFa/QRFP precursor has been characterized in various mammalian and non-mammalian species.

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.

A Three-Site Mechanism for Agonist/Antagonist Selective Binding to Vasopressin Receptors.

Molecular-dynamics simulations with metadynamics enhanced sampling reveal three distinct binding sites for arginine vasopressin (AVP) within its V2 -receptor (V2 R). Two of these, the vestibule and intermediate sites, block (antagonize) the receptor, and the third is the orthosteric activation (agonist) site. The contacts found for the orthosteric site satisfy all the requirements deduced from mutagenesis experiments.

Acute toxicities of pharmaceuticals toward green algae. mode of action, biopharmaceutical drug disposition classification system and quantile regression models.

The acute toxicities of 36 pharmaceuticals towards green algae were estimated from a set of quantile regression models representing the first global quantitative structure-activity relationships. The selection of these pharmaceuticals was based on their predicted environmental concentrations. An agreement between the estimated values and the observed acute toxicity values was found for several families of pharmaceuticals, in particular, for antidepressants.

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.