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PubMed Journal Database | Journal of chemical information and modeling RSS

23:36 EST 17th January 2019 | BioPortfolio

The US National Library of Medicine and National Institutes of Health manage PubMed.gov which comprises of more than 29 million records, papers, reports for biomedical literature, including MEDLINE, life science and medical journals, articles, reviews, reports and  books.

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Showing PubMed Articles 1–25 of 447 from Journal of chemical information and modeling

Computational In Vitro Toxicology Uncovers Chemical Structures Impairing Mitochondrial Membrane Potential.

Technological advances in molecular biology enable high-throughput screening (HTS) of large chemical libraries. These approaches have provided valuable toxicity data for many physiological responses, including mitochondrial dysfunction. While several quantitative structure-activity relationship (QSAR) models have been developed for mitochondrial dysfunction, there remains a need to identify specific chemical features associated with this response. Thus, the objective of this study was to identify chemical s...

Scaffold Effects on Halogen Bonding Strength.

Halogen bonds have become increasingly popular interactions in molecular design and drug discovery. One of its key features is the strong dependence of the size and magnitude of the halogen's σ-hole on the chemical environment of the ligand. The term σ-hole refers to a region of lower electronic density opposite to a covalent bond, e.g., the C-X bond. It is typically (but not always) associated with a positive electrostatic potential in close proximity to the extension of the covalent bond. Herein, we use...

CavVis - A field-of-view geometric algorithm for protein cavity detection.

Several geometric-based methods have been developed for the last two to three decades to detect and identify cavities (i.e., putative binding sites) on proteins, as needed to study protein-ligand interactions and protein docking. This paper introduces a new protein cavity method, called CavVis, which combines voxelization (i.e., a grid of voxels) and an analytic formulation of Gaussian surfaces that approximates the solvent-excluded surface (SES). This method builds upon visibility of points on protein surf...

Virtual Compound Libraries in Computer-Assisted Drug Discovery.

The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the last decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency, the need for more reliable vendors and accurate synthesis predicti...

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds", "bad actors" or "nuisance compounds". These compounds are typically aggregators, reactive compounds and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that pred...

InsectiPAD: A Web Tool Dedicated to Explore Physicochemical Properties and Evaluate Insecticide-likeness of Small Molecules.

The concept of insecticide-likeness is valuable to select more promising lead candidates during the early stages of drug discovery. We analyzed the physicochemical properties of commercial insecticides and optimized available drug-likeness scoring functions to quantitatively evaluate the insecticidal-like potential of compounds. An interactive platform named Insecticide Physicochemical-properties Analysis Database (InsectiPAD) was developed, which covers 495 approved insecticides and over 22,200 related phy...

A Brief Recent History of Women in Computational Chemistry at Vertex Pharmaceuticals.

There are countless reports citing the importance of diversity in the academic, industrial and government workplace (1, 2). But to achieve this goal, diverse role models need to have impact early in the education of young people encouraged and attracted to STEM-related studies.

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Datasets.

Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics and safety. While volume and chemotype coverage of public and corporate ADME-Tox databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyse those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends b...

Reparametrization of the COSMO Solvent Model for Semiempirical Methods PM6 and PM7.

An accurate description of solvation effects is of high importance in modeling biomolecular systems. Our main interest is to find an accurate yet efficient solvation model for semiempirical quantum-mechanical methods applicable to large protein-ligand complexes in the context of computer-aided drug design. We present a survey of readily available methods and a new reparametrization of the COSMO solvent model for PM6 and PM7 calculations in MOPAC. We have tested the reparametrized method on validation data s...

Kinetic Analysis of Hepatic Metabolism Using Hyperpolarized Dihydroxyacetone.

Hyperpolarized carbon-13 magnetic resonance (HP-MR) is a new metabolic imaging method the does not use ionizing radiation. Due to the inherent chemical specificity of MR, not only tracer uptake but also downstream metabolism of the agent is detected in a straightforward manner. HP [2-C]dihydroxyacetone (DHA) is a promising new agent that directly interrogates hepatic glucose metabolism. DHA has three metabolic fates in the liver: glucose production, glycerol production and potential inclusion into triglycer...

Computational Prediction of Site of Metabolism for UGT-catalyzed Reactions.

The investigation of metabolically liable sites of xenobiotics mediated by UDP-glucuronosyltransferases (UGT) is important for lead optimization in early drug discovery. However, it is time-consuming and costly to identify potential susceptible sites experimentally. In silico approaches are hence developed to predict site of metabolism (SOM) of UGT-catalyzed substrates. In the present work, four major types of reactions catalyzed by UGT were collected from the book "Handbook of Metabolic Pathways of Xenobio...

Exploring Tunable Hyperparameters for Deep Neural Network with Industrial ADME Data Sets.

Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machine learning models are built to understand quantitative structure activity relationships (QSAR) and predict molecular activities, including absorption, distribution, metabolism and excretion (ADME) properties, using only molecular structures. Previous reports hav...

How Can Interleukin-1 Receptor Antagonist Modulate Distinct Cell Death Pathways?

Multiple mechanisms of cell death exist (apoptosis, necroptosis, pyroptosis) and the subtle balance of several distinct proteins and inhibitors tightly regulates the cell fate towards one or the other pathway. Here, by combining co-immunoprecipitation, enzyme assays, and molecular simulations, we ascribe a new role, within this entangled regulatory network, to the interleukin-1 receptor antagonist (IL-1Ra). Our study enlightens that IL-1Ra, which usually inhibits the inflammatory effects of IL-1α/β by bin...

PathwayMap: Molecular pathway association with self-normalizing neural networks.

Drug discovery suffers from high attrition as compounds initially deemed as promising can later show ineffectiveness or toxicity due to a poor understanding of their activity profile. In this work we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external dataset provided by Novartis. We finally discuss the a...

Structural insights into characterizing binding sites in EGFR kinase mutants.

Over the last two decades epidermal growth factor receptor (EGFR) kinase has become an important target to treat non-small cell lung cancer (NSCLC). Currently, three generations of EGFR kinase-targeted small molecule drugs have been FDA approved. They nominally produce a response at the start of treatment and lead to a substantial survival benefit for patients. However, long-term treatment results in acquired drug resistance and further vulnerability to NSCLC. Therefore, novel EGFR kinase inhibitors that sp...

Energetic Fingerprinting of Ligand Binding to Paralogous Proteins: The Case of the Apoptotic Pathway.

Networks of biological molecules are key to cellular function, governing processes ranging from signal cascade propagation to metabolic pathway regulation. Genetic duplication processes give rise to sets of regulatory proteins that have evolved from a common ancestor, leading to interactomes whose dysregulation is often associated with disease. A better understanding of the determinants of specificity at interfaces shared by functionally related proteins is crucial to the rational design of novel pharmacoth...

Predicting the Magnitude of σ-holes Using VmaxPred, a Fast and Efficient Tool Supporting the Application of Halogen Bonds in Drug Discovery.

Halogen bonding (XB) as a modern molecular interaction has received increasing attention, not only in material sciences, but also in biological systems and drug discovery. Thus, there is a growing demand for fast, efficient, and easily applicable tailor-made tools supporting the use of halogen bonds in molecular design and medicinal chemistry. The potential strength of a halogen bond is dependent on several properties of the σ-hole donor, e.g. a (hetero)aryl halide, and the σ-hole acceptor, a nucleophile ...

Affinity Calculations of Cyclodextrin Host-Guest Complexes: Assessment of Strengths and Weaknesses of End-Point Free Energy Methods.

The end-point methods like MM/PBSA or MM/GBSA estimate the free energy of a biomolecule by combining its molecular mechanics energy with solvation free energy and entropy terms. Their performance largely depends on the particular system of interest and despite numerous attempts to improve their reliability that have resulted in many variants, there is still no clear alternative to improve their accuracy. On the other hand, the relatively small cyclodextrin host-guest complexes, for which high quality bindin...

Towards multi-scale modeling of proteins and bio-agglomerates: An orientation-sensitive diffusion model for the integration of Molecular Dynamics and the Discrete Element Method.

Most processes involved in biological and biotechnological systems spread over many scales in space and time. For example, the interaction of multiple enzymes in heterogenous enzymatic agglomerates or clusters, necessary for efficient enzymatic conversion, is of high interest for research and enzyme engineering. In order to understand and predict their overall behavior and performance, it is important to describe these scales as completely as possible, known as multi-scale modeling. While many different app...

Robustness and Efficiency of Poisson-Boltzmann Modeling on GPUs.

Poisson-Boltzmann equation (PBE)-based continuum electrostatics models have been widely used in modeling electrostatic interactions in biochemical processes, particularly in estimating protein-ligand binding affinities. Fast convergence of PBE solvers is crucial in binding affinity computations as numerous snapshots need to be processed. Efforts have been reported to develop PBE solvers on graphics processing units (GPUs) for efficient modeling of biomolecules, though only relatively simple successive over-...

Chemical Space and Biological Target Network of Anti-inflammatory Natural Products.

Natural products (NPs) are a promising source of anti-inflammatory molecules for the development of drugs. Despite there being an abundance of reports of large numbers of NPs having bioactivity in preliminary cell-based assays of anti-inflammatory potential, their further optimization and exploration is limited by lack of a comprehensive understanding of their effective scaffold structure or biological targets. To facilitate target-based studies of anti-inflammatory NPs, the details of 665 NPs reported to h...

LigandScout Remote: A new User-Friendly Interface for HPC and Cloud Resources.

High Performance Computing (HPC) clusters play a major role in scientific research. However, working with these clusters is often cumbersome, especially for researches without formal background in computer science. It requires preparation and transfer of the input data, manual gathering of results, and command-line expertise. Current approaches for improving accessibility to remote HPC clusters focus on providing web-based graphical front-ends, which allow to submit jobs to the distributed resource manageme...

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB) based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the...

Probing the Pharmacological Binding Sites of P-Glycoprotein Using Umbrella Sampling Simulations.

The human multidrug transporter P-glycoprotein (P-gp) transports over 200 chemically diverse substrates, influencing their bioavailability and tissue distribution. Pharmacological studies have identified both competitive and non-competitive P-gp substrates, but neither the precise location of the substrate binding sites, nor the basis of competitive and non-competitive interactions has been fully characterized. Here, potential of mean force (PMF) calculations are used to identify the transport-competent min...

Using membrane partitioning simulations to predict permeability of forty-nine drug-like molecules.

A simple descriptor calculated from molecular dynamics simulations of the membrane partitioning event is found to correlate well with experimental measurements of passive membrane permeation from the high throughput MDCK-LE assay using a dataset of 49 drug-like molecules. This descriptor approximates the energy cost of translocation across the hydrophobic membrane core (flip-flop), which for many molecules limits permeability. Performance is found to be superior in comparison to calculated properties such a...


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