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PubMed Journal Database | Journal of chemical information and modeling RSS

02:20 EDT 19th August 2018 | BioPortfolio

The US National Library of Medicine and National Institutes of Health manage PubMed.gov which comprises of more than 21 million records, papers, reports for biomedical literature, including MEDLINE, life science and medical journals, articles, reviews, reports and  books.  BioPortfolio aims to publish relevant information on published papers, clinical trials and news associated with users selected topics.

For example view all recent relevant publications on Epigenetics and associated publications and clincial trials.

Showing PubMed Articles 1–25 of 432 from Journal of chemical information and modeling

How Reactive are Druggable Cysteines in Protein Kinases?

Targeted covalent-inhibitors (TCIs) have been successfully developed as high-affinity and selective inhibitors of enzymes of the protein kinase family. These drugs typically act by undergoing an electrophilic addition with an active site cysteine residue, so design of a TCI begins with the identification of a "druggable" cysteine. These electrophilic additions generally require the deprotonation of the thiol to form a reactive anionic thiolate, so the acidity of the residue is a critical factor. Few experim...

Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery.

The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, method comparison is difficult because of various flaws of the currently employed evaluation metrics. We propose an evaluation metric for generative models called Fréchet ChemblNet distance (FCD). The FCD's advantage over previous metrics is that it can detect whether generated molecules are diverse and have similar chemical and biological properties as real mole...

Detecting Proline and Non-Proline Cis-Isomers In Protein Structures from Sequences Using Deep Residual Ensemble Learning.

It has been long established that cis conformations of amino acid residues play many biologically important roles despite their rare occurrence in protein structure. Due to this rarity, few methods have been developed for predicting cis-isomers from protein sequences, most of which are based on outdated datasets and lack the means for independent testing. In this work, using a database of >10000 high-resolution protein structures, we update the statistics of cis-isomers and develop a sequence-based predicti...

Assimilating Radial Distribution Functions to Build Water Models with Improved Structural Properties.

The structural properties of three and four site water models are improved by extending the ForceBalance parameterization code to include a new methodology allowing for the targeting of any radial distribution function (RDF) during the parametrization of a force field. The mean squared difference (MSD) between the experimental and simulated RDFs contributes to an objective function, allowing for the systematic optimization of force field parameters to reach closer overall agreement with experiment. RDF fitt...

Exploring Drugbank in Virtual Reality Chemical Space.

The recent general availability of low-cost virtual reality headsets, and accompanying 3D engine support, presents an opportunity to bring the concept of chemical space into virtual environments. While virtual reality applications represent a category of widespread tools in other fields, their use in the visualization and exploration of abstract data such as chemical spaces has been experimental. In our previous work we established the concept of interactive 2D maps of chemical spaces, followed by interacti...

Efficiency of Stratification for Ensemble Docking using Reduced Ensembles.

Molecular docking can account for receptor flexibility by combining the docking score over multiple rigid receptor conformations, such as snapshots from a molecular dynamics simulation. Here, we evaluate a number of common snapshot selection strategies using a quality metric from stratified sampling, the efficiency of stratification, which compares the variance of a selection strategy to simple random sampling. We also extend the metric to estimators of exponential averages (which involve an exponential tra...

Machine Learning in Drug Discovery.

Virtual Screening and Experimental Testing of B1 Metallo-β-lactamase Inhibitors.

The global rise of metallo-β-lactamases (MBLs) is problematic due to their ability to inactivate most β-lactam antibiotics. MBL inhibitors that could be co-administered with and restore the efficacy of β-lactams are highly sought after. In this study, we employ virtual screening of candidate MBL inhibitors without thiols or carboxylates to avoid off-target effects using the Avalanche software package, followed by experimental validation of the selected compounds. As target enzymes we chose the clinically...

All-Hydrocarbon Staples and Their Effect over Peptide Conformation Under Different Force Fields.

Olefinic staples enhance alpha-helical content and conformational stability in peptides, maintaining a structural scaffold that allows the emulation of specific regions of protein surfaces for therapeutical purposes. The ability to anticipate the efficacy of the staple addition to a peptide through computational simulations may contribute to lower the costs associated with rational drug design. We evaluated the capability of different force fields to reproduce the effect of all-hydrocarbon staples on molecu...

Defining the specificity of carbohydrate-protein interactions by quantifying functional group contributions.

Protein-carbohydrate interactions are significant in a wide range of biological processes, disruption of which has been implicated in many different diseases. The capability of glycan-binding proteins (GBP) to specifically bind to the corresponding glycans allows GBPs to be utilized in glycan biomarker detection or conversely to serve as targets for therapeutic intervention. However, understanding the structural origins of GBP specificity has proven to be challenging due to their typically low binding affin...

How Does Aqueous Choline-O-Sulfate Solution Nullify the Action of Urea in Protein Denaturation?

Choline-O-sulfate (COS) acts as a protecting osmolyte in several plants, fungal and bacterial species. Classical molecular dynamics simulation is performed to examine the molecular mechanism by which COS molecules counteract urea-conferred denaturation of S-peptide analogue. The calculations of root mean squared deviation, the radius of gyration of Cα atom and solvent accessible surface area of the peptide heavy atoms imply that the 4-12 residues of the peptide in pure water remain in helical conformation ...

Addendum to BiKi Life Sciences: A New Suite for Molecular Dynamics and Related Methods in Drug Discovery.

Virtual Screening Against Carbohydrate-binding Proteins: Evaluation and Application to Bacterial Burkholderia ambifaria Lectin.

Bacterial adhesion to human epithelia via lectins constitutes a therapeutic opportunity to prevent infection. Specifically, BambL (the lectin from Burkholderia ambifaria) is implicated in cystic fibrosis, where lectin-mediated bacterial adhesion to fucosylated lung epithelia is suspected to play an important role. We have employed structure-based virtual screening to identify inhibitors of BambL-saccharide interaction with potential therapeutic value. In order to enable such discovery, a virtual screening p...

Electrostatic Polarization Effect on Cooperative Aggregation of Full Length Human Islet Amyloid.

Amyloid aggregation initiates from a slow nucleation process, where the association of monomers is unfavorable in energetics. In principle, the enthalpy change for aggregation should compensate the entropy loss as new monomers attach to formed oligomers. However, the classical force fields with fixed point charges failed to yield the correct enthalpy change due to the lack of electrostatic polarization effect on amyloid aggregation. In this work, we performed molecular dynamics simulation for the full-lengt...

Molecular dynamics simulation of cyclooxygenase-2 complexes with indomethacin closo-carborane analogs.

Molecular dynamics simulation of carborane-containing ligands in complex with target enzymes is a challenging task due to the unique structure and properties of the carborane substituents, and relative lack of appropriate experimental data to help assess the quality of carborane force field parameters. Here, we report results from energy minimization calculations for a series of carborane-amino acid complexes using carborane force field parameters published previously in the literature and adapted for use w...

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

The majority of computational methods for predicting toxicity of chemicals are typically based on 'non-mechanistic' cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically employing 'black-box' mathematical algorithms. Nonetheless, such machine learning models, while having lower generalization capacity and interpretability, typically achieve a very high accuracy in predicting various toxicity endpoints, as unambiguously reflecte...

Pushing the Limits of Computational Structure-Based Drug Design with a Cryo-EM Structure: the Ca+2 Channel α2δ-1 Subunit as a Test Case.

Cryo electron microscopy (cryo-EM) is emerging as a real alternative for structural elucidation. In spite of this, very few cryo-EM structures have been described so far as successful platforms for in silico drug design. Gabapentin and pregabalin are some of the most successful drugs in the treatment of epilepsy and neuropathic pain. Although both are in clinical use and are known to exert their effects by binding to the regulatory α2δ subunit of voltage gated calcium channels, their binding modes have ne...

A Novel Consensus Docking Strategy to Improve the Ligand Pose Prediction.

Molecular docking, which mainly includes pose prediction and binding affinity calculation, has become an important tool for assisting structure-based drug design. Correctly predicting the ligand binding pose to a protein target enables the estimation of binding free energy using various tools. Previous studies have shown that the consensus method can be used to improve the docking performance in respect of compound scoring and pose prediction. In this report, a novel consensus docking strategy was proposed,...

Understanding of Structure and Thermodynamics of Melamine Association in Aqueous Solution From a Unified Theoretical and Experimental Approach.

The aggregation propensity of melamine molecules in aqueous solutions in a range of melamine concentrations is investigated by means of a combination of theoretical and experimental approach. It is observed that the hydrogen bonding interactions of sp3 nitrogen atoms of one melamine with sp2 nitrogen atoms of another melamine play a major role in the melamine association. This finding is complemented by the observed favorable electrostatic energies between melamine molecules. The estimation of the orientati...

3D-QSAR Modelling and Synthesis of New Fusidic Acid Derivatives as Antiplasmodial Agents.

Wide spread Plasmodium falciparum (P. falciparum) resistance has compromised existing antimalarial therapies to varying degrees. Novel agents, able to circumvent antimalarial drug resistance, are therefore needed. Fusidic acid is a unique antibiotic with a unique mode of action, which has shown weak in vitro antiplasmodial activity. Towards identifying new fusidic acid derivatives with superior antiplasmodial activity, a 3D-QSAR model was developed based on the antiplasmodial activity of previously synthesi...

Placement of Water Molecules in Protein Structures: From Large-Scale Evaluations to Single-Case Examples.

Water molecules are of great importance for the correct representation of ligand binding interactions. Throughout the last years, water molecules and their integration into drug design strategies have received increasing attention. Nowadays a variety of tools are available to place and score water molecules. However, the most frequently applied software solutions require substantial computational resources. In addition, none of the existing methods has been rigorously evaluated on the basis of a large numbe...

Conditional molecular design with deep generative models.

Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semi-supervised variational autoencoder trained on a set of existing molecules with...

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets, but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of ...

Steered Molecular Dynamics Simulation in Rational Drug Design.

Conventional de novo drug design is time consuming, laborious, and resource intensive. In recent years, emerging in-silico approaches have been proven to be critical to accelerate the process of bringing drugs to market. Molecular dynamics (MD) simulations of single molecule and molecular complexes have been commonly applied to achieve accurate binding modes and binding energies of drug-receptor interactions. A derivative of MD, namely steered molecular dynamics (SMD), has been demonstrated as a promising t...

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of HIV-infected patients' lifespans. Methods capable of predicting resistance susceptibility in the development of compounds are therefore in great need. Targeting the major reverse transcription residues Y181, K103 and L100, we used the biologi-cal activities of compounds against these enzymes and the wild type reverse transcriptase to create Naïve Bayes Net-works. Through this machine learning app...


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