3,797 results match your criteria Journal of Chemical Information and Modeling [Journal]


A New Class of Superhalogen Based Anion Receptor in Li-ion Battery Electrolytes.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

In the search for new additives in Li-ion battery electrolytes especially for LiPF6 and LiClO4, we have theoretically designed boron based complexes by coupling with different heterocyclic ligands. The validation of the formation of modeled compounds involves reproduction of available experimentally reported absolute magnetic shielding and chemical shift values for different boron-complexes. As compared to the commonly used tris (pentaflurophenyl) borane, our designed compounds suggest that the complexes like B[C2HBNO(CN)2]3, B[C2HBNS(CN)2]3 and B[C4H3BN(CN)2]3 are promising additives. Read More

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http://dx.doi.org/10.1021/acs.jcim.9b00035DOI Listing
February 2019

Discovering Putative Protein Targets of Small Molecules: A Study of the p53 Activator Nutlin.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

Small molecule drugs bind to a pocket in disease causing target proteins based on complementarity in shape and physicochemical properties. There is a likelihood that other proteins could have binding sites that are structurally similar to the target protein. Binding to these other proteins could alter their activities leading to off target effects of the drug. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00762DOI Listing
February 2019

Tuning interaction parameters of thermoplastic polyurethanes in a binary solvent to achieve precise control over micro-phase separation.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

Thermoplastic polyurethanes (TPUs) are designed using a large variety of basic building blocks but are only synthesized in a limited number of solvent systems. Understanding the behavior of the copolymers in a selected solvent system is of particular interest to tune the intricate balance of micro-phase separation/mixing, which is the key mechanism behind the structure formation in TPUs. Here, we present a computationally efficient approach for selecting TPU building blocks and solvents based on their Flory-Huggins interaction parameters for a precise control over the micro-phase separation/mixing. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00781DOI Listing
February 2019

Projector-based embedding eliminates density functional dependence for QM/MM calculations of reactions in enzymes and solution.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

Combined quantum mechanics/molecular mechanics (QM/MM) methods are increasingly widely utilized in studies of reactions in enzymes and other large systems. Here, we apply a range of QM/MM methods to investigate the Claisen rearrangement of chorismate to prephenate, in solution, and in the enzyme chorismate mutase. Using projector-based embedding in a QM/MM framework, we apply treatments up to the CCSD(T) level. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00940DOI Listing
February 2019

Charge Density in Enzyme Active Site as a Descriptor of Electrostatic Preorganization.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

Large protein macromolecules in enzymatic catalysis have been shown to exert a specific electric field that reduces the reorganization energy upon barrier crossing and thus reduces the reaction free energy barrier. In this work we suggest that the charge density in the active site of an enzyme investigated using formalisms embodied by the quantum theory of atoms in molecules (QTAIM) provides a sensitive and quantum mechanically rigorous probe of electrostatic preorganization. We focus on the active site of ketosteroid isomerase, a well-studied enzyme for which electrostatic preorganization has been modeled theoretically and studied experimentally. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00958DOI Listing
February 2019

Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System.

J Chem Inf Model 2019 Feb 21. Epub 2019 Feb 21.

Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide range of pH-s were derived in the current study. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00833DOI Listing
February 2019

Mutations in Parkinson's Disease Associated Protein DJ-1 Alter the Energetics of DJ-1 Dimerization.

J Chem Inf Model 2019 Feb 21. Epub 2019 Feb 21.

Patients suffering from familial Parkinson's disease are linked to mutated DJ-1 protein. Wild-type DJ-1 occurs as a homodimer, which appears to be crucial for its function. It has been established that mutation (viz. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00687DOI Listing
February 2019

De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping.

J Chem Inf Model 2019 Feb 20. Epub 2019 Feb 20.

Here we show that Generative Topographic Mapping (GTM) can be used to explore the latent space of the SMILES-based autoencoders and generate focused molecular libraries of interest. We have built a sequence-to-sequence neural network with Bidirectional Long Short-Term Memory layers and trained it on the SMILES strings from ChEMBL23. Very high reconstruction rates of the test set molecules were achieved (>98%), which are comparable to the ones reported in related publications [2,3]. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00751DOI Listing
February 2019

Interpretation of QSAR Models By Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

J Chem Inf Model 2019 Feb 19. Epub 2019 Feb 19.

Most chemists would agree that the ability to interpret a Quantitative Structure Activity Relationship (QSAR) model is as important as the ability of the model to make accurate predictions. One type of interpretation is coloration of atoms in molecules according to the contribution of the atom to the predicted activity, as in "heat maps". The ability to determine which parts of a molecule increase the activity in question and which decrease it should be useful to chemists who want to modify the molecule. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00825DOI Listing
February 2019

Hydrogen Bonding Promoted Tautomerism Between Azo and Hydrazone Forms in Calcon with Multi-Stimuli Responsiveness and Biocompatibility.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

Realization of multi-stimuli responsiveness in one molecule remains a challenge due to the difficulty in understanding and control of comprehensive interplay between the external stimuli and the subtle conformation changes. The coexistence of dynamic bonding interactions, hydroxyl group and the azo chromophore in calcon causes the multi-stimuli responsiveness to external stimuli including temperature, pH-variation and light-irradiation. Density functional theory (DFT), time-dependent DFT (TDDFT), and various molecular dynamics (MD) simulations are employed to systematically investigate the azo-hydrazone tautomerism and E-Z isomerization. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00985DOI Listing
February 2019

Chemical insight on decreased sensitivity of CL-20/TNT co-crystal revealed by ReaxFF MD simulations.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

Understanding the underlying mechanisms on sensitivity-decrease of the CL-20/TNT co-crystal is essential for wide applications of the promising high-energetic CL-20. This work presents the chemical scenario of CL-20/TNT thermolysis obtained from ReaxFF molecular dynamics simulations. Facilitated by the unique VARxMD for reaction analysis, the interplay reactions between CL-20 and TNT responsible for the sensitivity-decrease of CL-20/TNT was first revealed. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00952DOI Listing
February 2019

Structural Features and Energetics of Periplasmic Entrance Opening of the Outer Membrane Channel TolC Revealed by Molecular Dynamics Simulation and Markov State Model Analysis.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

TolC is a channel protein responsible for substrate translocation across outer membrane, and it is also a part of the tripartite multidrug efflux pumps in Gram-negative bacteria. The crystal structure of TolC shows that the periplasmic entrance is tightly closed in the resting state, while substrate translocation definitely requires the entrance to open. How the occluded periplasmic entrance opens to allow passage of substrates remains elusive. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00957DOI Listing
February 2019

Quantitative Structure-Price Relationships (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

In recent years, the field of quantitative structure-activity/property modeling (QSAR/QSPR) has developed into a stable technology capable of reliably predicting new bioactive molecules. With the availability of inexpensive commercial sources of both synthetic chemicals and bioactivity assays, a cheminformatics-savvy scientist can readily establish a virtual drug discovery enterprise. Not only a skilled computational chemist can develop a computer-aided drug discovery pipeline, but also acquire or have them made inexpensively for economic screen of desired on-target activity, critical off-target effects and essential drug-likeness properties. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00747DOI Listing
February 2019

Changes in Microenvironment Modulate the B- to A-DNA Transition.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

B- to A-DNA transition is known to be sensitive to the macroscopic properties of the solution, such as salt and ethanol concentrations. Microenvironmental effects on DNA conformational transition have been broadly studied. Providing an intuitive picture of how DNA responds to environmental changes is, however, still needed. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00885DOI Listing
February 2019

DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network.

J Chem Inf Model 2019 Feb 21. Epub 2019 Feb 21.

School of Pharmaceutical Sciences & School of Data and Computer Science , Sun Yat-Sen University , 132 East Circle at University City , Guangzhou 510006 , China.

In the drug discovery process, unstable compounds in storage can lead to false positive or false negative bioassay conclusions. Prediction of the chemical stability of a compound by de novo methods is complex. Chemical instability prediction is commonly based on a model derived from empirical data. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00672DOI Listing
February 2019

Structure and Dynamics of the CRISPR-Cas9 Catalytic Complex.

Authors:
Giulia Palermo

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

CRISPR-Cas9 is a bacterial immune system with exciting applications for genome editing. In spite of extensive experimental characterization, the active site chemistry of the RuvC domain - which performs DNA cleavages - has remained elusive. Its knowledge is key for structure-based engineering aimed at improving DNA cleavages. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00988DOI Listing
February 2019

Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking.

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00959DOI Listing
February 2019

Classification of Cyclooxygenase-2 Inhibitors using Support Vector Machine and Random Forest Methods.

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

This work reports the classification study conducted on the biggest COX-2 inhibitor dataset so far. Using 2925 diverse COX-2 inhibitors collected from 168 literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF) to develop twelve classification models. The best SVM and RF models resulted in MCC values of 0. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00876DOI Listing
February 2019

Shape-Based Generative Modeling for de-novo Drug Design.

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

In this work we propose a machine learning approach to generate novel molecules starting from a seed compound, its 3D shape and pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of de-novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00706DOI Listing
February 2019

Characterization of differential dynamics, specificity, and allostery of lipoxygenase family members.

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

Accurate modeling of structural dynamics of proteins and their differentiation across different species can help us understand generic mechanisms of function shared by family members and the molecular basis of the specificity of individual members. We focused here on the family of lipoxygenases, enzymes that catalyze lipid oxidation, the mammalian and bacterial structures of which have been elucidated. We present a systematic method of approach for characterizing the sequence, structure, dynamics and allosteric signaling properties of these enzymes using a combination of structure-based models and methods, and bioinformatics tools applied to a dataset of 88 structures. Read More

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http://pubs.acs.org/doi/10.1021/acs.jcim.9b00006
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http://dx.doi.org/10.1021/acs.jcim.9b00006DOI Listing
February 2019
2 Reads

DeepDDG: Predicting the Stability Change of Protein Point Mutations using Neural Networks.

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural-network-based method, for use in the prediction of changes in the stability of proteins due to point mutations. The neural network was trained on more than 5700 manually curated experimental data points and was able to obtain a Pearson correlation coefficient of 0. Read More

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http://pubs.acs.org/doi/10.1021/acs.jcim.8b00697
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http://dx.doi.org/10.1021/acs.jcim.8b00697DOI Listing
February 2019
4 Reads

Molecular Structure Extraction From Documents Using Deep Learning.

J Chem Inf Model 2019 Feb 13. Epub 2019 Feb 13.

Chemical structure extraction from documents remains a hard problem due to both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines that perform reasonably well generally, but still routinely encounter situations where recognition rates are not yet satisfactory and systematic improvement is challenging. Complications impacting performance of current approaches include the diversity in visual styles used by various software to render structures, the frequent use of ad hoc annotations, and other challenges related to image quality, including resolution and noise. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00669DOI Listing
February 2019

Both Reactivity and Accessibility Are Important in Cytochrome P450 Metabolism: A Combined DFT and MD Study of Fenamic Acids in BM3 Mutants.

J Chem Inf Model 2019 Feb 13. Epub 2019 Feb 13.

Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences , University of Copenhagen , Universitetsparken 2 , DK-2100 Copenhagen , Denmark.

Cytochrome P450 102A1 from Bacillus megaterium (BM3) is a fatty acid hydroxylase that has one of the highest turnover rates of any mono-oxygenase. Recent studies have shown how mutants of BM3 can produce metabolites of known drug compounds similar to those observed in humans. Single-point mutations in the binding pocket change the regioselective metabolism of fenamic acids from aromatic hydroxylation to aliphatic hydroxylation. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00750DOI Listing
February 2019

Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States.

To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing grid and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes. In total there are ∼10000 simulated complexes, ∼1 million replicas, and ∼2000 μs of aggregated MD simulations. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00817DOI Listing
February 2019
2 Reads

Imputation of Assay Bioactivity Data Using Deep Learning.

J Chem Inf Model 2019 Feb 21. Epub 2019 Feb 21.

Intellegens , Eagle Labs , Chesterton Road , Cambridge CB4 3AZ , United Kingdom.

We describe a novel deep learning neural network method and its application to impute assay pIC values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00768DOI Listing
February 2019

Conformator: A Novel Method for the Generation of Conformer Ensembles.

J Chem Inf Model 2019 Feb 12. Epub 2019 Feb 12.

Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany.

Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00704DOI Listing
February 2019

Assessing the Conformational Equilibrium of Carboxylic Acid via Quantum Mechanical and Molecular Dynamics Studies on Acetic Acid.

J Chem Inf Model 2019 Feb 21. Epub 2019 Feb 21.

Department of Chemistry , University of California , Irvine , California 92697 , United States.

Accurate hydrogen placement in molecular modeling is crucial for studying the interactions and dynamics of biomolecular systems. The carboxyl functional group is a prototypical example of a functional group that requires protonation during structure preparation. To our knowledge, when in their neutral form, carboxylic acids are typically protonated in the syn conformation by default in classical molecular modeling packages, with no consideration of alternative conformations, though we are not aware of any careful examination of this topic. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00835DOI Listing
February 2019
1 Read

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

School of Natural and Environmental Sciences , Newcastle University , Newcastle upon Tyne NE1 7RU , United Kingdom.

Modern molecular mechanics force fields are widely used for modeling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. However, for molecules outside the training set, the parameters are potentially inaccurate and it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00767DOI Listing
February 2019

OCTP: A Tool for On-the-Fly Calculation of Transport Properties of Fluids with the Order- n Algorithm in LAMMPS.

J Chem Inf Model 2019 Feb 21. Epub 2019 Feb 21.

Engineering Thermodynamics, Process & Energy Department, Faculty of Mechanical, Maritime and Materials Engineering , Delft University of Technology , Leeghwaterstraat 39 , 2628CB Delft , The Netherlands.

We present a new plugin for LAMMPS for on-the-fly computation of transport properties (OCTP) in equilibrium molecular dynamics. OCTP computes the self- and Maxwell-Stefan diffusivities, bulk and shear viscosities, and thermal conductivities of pure fluids and mixtures in a single simulation. OCTP is the first implementation in LAMMPS that uses the Einstein relations combined with the order- n algorithm for the efficient sampling of dynamic variables. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00939DOI Listing
February 2019

Stacked Generalization with Applicability Domain Outperforms simple QSAR on in vitro Toxicological Data.

J Chem Inf Model 2019 Feb 8. Epub 2019 Feb 8.

The development of in silico tools able to predict bioactivity and toxicity of chemical substances is a powerful solution envisioned to asess toxicity as early as possible. To enable the development of such tools, the ToxCast program has generated and made publicly available in vitro bioactivity data for thousands of compounds. The goal of the present study is to characterize and explore the data from ToxCast in terms of Machine Learning capacity. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00553DOI Listing
February 2019
1 Read

Feature Engineering for Materials Chemistry-Does Size Matter?

J Chem Inf Model 2019 Feb 20. Epub 2019 Feb 20.

ANU Supercomputer Facility , Leonard Huxley Building 56, Mills Road , Canberra , ACT 2601 , Australia.

The effects of structural featurizers in the prediction of band gaps have been investigated through machine learning by application to a silver nanoparticle data set and 2254 potential light-harvesting materials with known band gaps. Elemental properties were extended with structural features via Voronoi polyhedra, allowing for neighbor effects and thus presumably giving a better representation of the extended system. However, we did not find any noticeably significant difference in the predictive performance of our model. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00977DOI Listing
February 2019

Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial , Universitat Politècnica de Catalunya , 08028 Barcelona , Spain.

Binding prediction between targets and drug-like compounds through deep neural networks has generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classification models is still an issue to be addressed. In this work, we explored how different cross-validation strategies applied to data from different molecular databases affect to the performance of binding prediction proteochemometrics models. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00663DOI Listing
February 2019

Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors.

J Chem Inf Model 2019 Feb 22. Epub 2019 Feb 22.

Data Science and AI, Drug Safety & Metabolism , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden.

Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00724DOI Listing
February 2019

Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection.

J Chem Inf Model 2019 Feb 20. Epub 2019 Feb 20.

Department of Chemistry , Michigan State University , 578 S. Shaw Lane , East Lansing , Michigan 48824 , United States.

Knowledge-based potentials generally perform better than physics-based scoring functions in detecting the native structure from a collection of decoy protein structures. Through the use of a reference state, the pure interactions between atom/residue pairs can be obtained through the removal of contributions from ideal-gas state potentials. However, it is a challenge for conventional knowledge-based potentials to assign different importance factors to different atom/residue pairs. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00734DOI Listing
February 2019

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

Genomic Medicine Institute, Lerner Research Institute , Cleveland Clinic , Cleveland , Ohio 44106 , United States.

Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00769DOI Listing
February 2019

Heuristics from Modeling of Spectral Overlap in Förster Resonance Energy Transfer (FRET).

J Chem Inf Model 2019 Feb 4. Epub 2019 Feb 4.

Department of Chemistry , North Carolina State University , Raleigh , North Carolina 27695-8204 , United States.

Among the photophysical parameters that underpin Förster resonance energy transfer (FRET), perhaps the least explored is the spectral overlap term ( J). While by definition J increases linearly with acceptor molar absorption coefficient (ε in M cm), is proportional to wavelength (λ), and depends on the degree of overlap of the donor fluorescence and acceptor absorption spectra, the question arose as to the value of J for the case of perfect spectral overlap versus that for representative fluorophores with incomplete spectral overlap. Here, Gaussian distributions of absorption and fluorescent spectra have been modeled that encompass varying degrees of overlap, full-width-at-half-maximum (fwhm), and Stokes shift. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00753DOI Listing
February 2019
3.738 Impact Factor

In Silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

Chemical Computing Group , Montreal , Quebec H3A 2R7 , Canada.

In this work, four methods are described and validated for generating in silico ensembles of PROTAC-mediated ternary complexes. Filters based on characteristics of the proposed ternary complexes are developed to identify those that resemble known crystal structures. We then show how to use these modeling techniques a priori to discriminate the PROTAC-mediated degradation behavior of a mutant protein vs its wild type, of three closely related targets, and among three different PROTAC molecules. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00872DOI Listing
February 2019
1 Read

BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping.

J Chem Inf Model 2019 Feb 12. Epub 2019 Feb 12.

Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States.

Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program. Read More

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http://pubs.acs.org/doi/10.1021/acs.jcim.9b00020
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http://dx.doi.org/10.1021/acs.jcim.9b00020DOI Listing
February 2019
2 Reads

DNAzymes at Work: A DFT Computational Investigation on the Mechanism of 9DB1.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

Dipartimento di Chimica "G. Ciamician" , Alma Mater Studiorum - Università di Bologna , V. F. Selmi 2, 40126 Bologna , Italy.

The 9DB1 DNAzyme follows an addition-elimination (A+D) two-step mechanism, involving a phosphorane intermediate, where the 3'-hydroxyl group (nucleophile) of one RNA fragment attacks the 5'-triphosphate of another RNA fragment. This mechanism does not involve a divalent metal cation in agreement with the experimental evidence. The process is assisted by two proton transfers that activate the nucleophile (first step) and the leaving group (second step). Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00815DOI Listing
February 2019

SPOT-Peptide: Template-Based Prediction of Peptide-Binding Proteins and Peptide-Binding Sites.

J Chem Inf Model 2019 Feb 14. Epub 2019 Feb 14.

School of Information and Communication Technology , Griffith University , Southport , QLD 4222 , Australia.

Peptide-binding domains have been successfully targeted in therapeutic applications. However, many peptide-binding proteins (PBPs) remain uncharacterized. Computational prediction of peptide-domain interfaces is challenging due to short lengths, lack of well-defined structures, and limited conservation of peptide motifs. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00777DOI Listing
February 2019

Cys.sqlite: A Structured-Information Approach to the Comprehensive Analysis of Cysteine Disulfide Bonds in the Protein Databank.

J Chem Inf Model 2019 Feb 15. Epub 2019 Feb 15.

Applied Chemicals and Materials Division , National Institute of Standards and Technology , Boulder , Colorado 80305 , United States.

Cysteine is a multifaceted amino acid that is central to the structure and function of many proteins. A disulfide bond formed between two cysteines restrains protein conformations through the strong covalent bond and torsions about the bond that prefer, energetically, ±90°. In this study, we transform over 30 000 Protein Databank files (PDBx/mmCIFs) into a single file, the SQLite database (Cys. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00950DOI Listing
February 2019

Mechanistic Reactivity Descriptors for the Prediction of Ames Mutagenicity of Primary Aromatic Amines.

J Chem Inf Model 2019 Feb 13. Epub 2019 Feb 13.

Bayer AG , Pharmaceuticals R&D , 42096 Wuppertal , Germany.

Pharmaceutical products are often synthesized by the use of reactive starting materials and intermediates. These can, either as impurities or through metabolic activation, bind to the DNA. Primary aromatic amines belong to the critical classes that are considered potentially mutagenic in the Ames test, so there is a great need for good prediction models for risk assessment. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00758DOI Listing
February 2019
1 Read

Alanine Scanning Effects on the Biochemical and Biophysical Properties of Intrinsically Disordered Proteins: A Case Study of the Histidine to Alanine Mutations in Amyloid-β.

J Chem Inf Model 2019 Feb 7. Epub 2019 Feb 7.

Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine , University of South Florida , Tampa , Florida 33612 , United States.

Alanine scanning is a tool in molecular biology that is commonly used to evaluate the contribution of a specific amino acid residue to the stability and function of a protein. Additionally, this tool is also used to understand whether the side chain of a specific amino acid residue plays a role in the protein's bioactivity. Furthermore, computational alanine scanning methods are utilized to predict the thermodynamic properties of proteins. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00926DOI Listing
February 2019

Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors.

J Chem Inf Model 2019 Feb 12. Epub 2019 Feb 12.

National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , Rockville , Maryland 20850 , United States.

Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. Read More

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http://pubs.acs.org/doi/10.1021/acs.jcim.8b00617
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http://dx.doi.org/10.1021/acs.jcim.8b00617DOI Listing
February 2019
4 Reads
3.738 Impact Factor

Molecular Mechanism Regarding Allosteric Modulation of Ligand Binding and the Impact of Mutations on Dimerization for CCR5 Homodimer.

J Chem Inf Model 2019 Feb 13. Epub 2019 Feb 13.

College of Chemistry , Sichuan University , Chengdu 610064 , People's Republic of China.

In this work, we combined accelerated molecular dynamics (aMD) and conventional molecular dynamics (cMD) simulations coupled with the potential of mean force (PMF), correlation analysis, principal component analysis (PCA), and protein structure network (PSN) to study the effects of dimerization and the mutations of I52V and V150A on the CCR5 homodimer, in order to elucidate the mechanism regarding cooperativity of the ligand binding between two protomers and to address the controversy about the mutation-induced dimer-separation. The results reveal that the dimer with interface involved in TM1, TM2, TM3, and TM4 is stable for the CCR5 homodimer. The dimerization induces an asymmetric impact on the overall structure and the ligand-binding pocket. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00850DOI Listing
February 2019

Computational Insights into the Catalytic Mechanism of Bacterial Carboxylic Acid Reductase.

J Chem Inf Model 2019 Feb 12. Epub 2019 Feb 12.

Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , 32 West Seventh Avenue, Tianjin Airport Economic Area , Tianjin 300308 , China.

Multidomain carboxylic acid reductases (CARs) can reduce a wide range of carboxylic acids to the corresponding aldehydes in the presence of ATP and NADPH. Recent X-ray structures of the individual (di)domains of Segniliparus rugosus CAR (SrCAR) shed light on the catalysis mechanism and revealed domain dynamics during the different states of the reaction. However, the details of the catalytic mechanism of each step operated by the corresponding domains are still elusive. Read More

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http://pubs.acs.org/doi/10.1021/acs.jcim.8b00763
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http://dx.doi.org/10.1021/acs.jcim.8b00763DOI Listing
February 2019
4 Reads

Calculating Single-Channel Permeability and Conductance from Transition Paths.

J Chem Inf Model 2019 Jan 28. Epub 2019 Jan 28.

Permeability and conductance are the major transport property of membrane channels, quantifying the rate of channel crossing by the solute. It is highly desirable to calculate these quantities in all-atom molecular dynamics simulations. When the solute crossing rate is low, however, direct methods would require prohibitively long simulations, and one thus typically adopts alternative strategies based on the free energy of single solute along the channel. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00914DOI Listing
January 2019

Computing the Pathogenicity of Alzheimer's Disease Presenilin 1 Mutations.

J Chem Inf Model 2019 Feb 11. Epub 2019 Feb 11.

Department of Chemistry , Technical University of Denmark , DK-2800 Kongens Lyngby , Denmark.

Alzheimer's disease (AD) is one of the major global health challenges of the 21st century. More than 200 distinct mutations in presenilin 1 (PSEN1) cause severe early-onset familial AD (FAD) and are thus of central interest to the etiology of AD. PSEN1 is the catalytic subunit of γ-secretase that produces β-amyloid peptide (Aβ), and the mutations tend to increase the produced Aβ/Aβ ratio. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00896DOI Listing
February 2019
4 Reads

Radical Scavenging Activity of Natural-Based Cassaine Diterpenoid Amides and Amines.

J Chem Inf Model 2019 Feb 6. Epub 2019 Feb 6.

The radical scavenging capacities of four new cassaine diterpenoid amides including 3β-hydroxydinorerythrosuamide (1), 3β-acetoxydinorerythrosuamide (2), 3β-tigloyloxydinorerythrosuamide (3), and 6α-hydroxydinorcassamide (4) present in leaf extract and four new cassaine diterpenoid amines namely erythroformine A (5), erythroformine B (6), 6α-hydroxy-nor-cassamine (7), and nor-erythrosuamine (8) recently identified in the extract of the bark of Erythrophleum fordii were elucidated using density functional theory (DFT) method. Different thermochemical properties characterizing antioxidant potential including bond dissociation enthalpy (BDE), proton affinity (PA), and adiabatic ionization potential (IP) were calculated at the B3LYP/6-311G(d,p) level of theory. Scavenging reaction mechanisms of cassaine diterpenes toward HOO radical including formal hydrogen transfer (FHT; either hydrogen atom transfer (HAT) or proton coupled electron transfer (PCET)), radical adduct formation (RAF), single electron transfer (SET), and proton transfer (PT) were studied in the gas phase, water, and benzene. Read More

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http://dx.doi.org/10.1021/acs.jcim.8b00847DOI Listing
February 2019