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Journal of Chemical Information and Modeling

Jonghun Won, Gyu Rie Lee, Hahnbeom Park, Chaok Seok
The second extracellular loops (ECL2s) of G-protein-coupled receptors (GPCRs) are often involved in GPCR functions, and their structures have important implications in drug discovery. However, structure prediction of ECL2 is difficult because of its long length and the structural diversity among different GPCRs. In this study, a new ECL2 conformational sampling method involving both template-based and ab initio sampling was developed. Inspired by observation of similar ECL2 structures of closely related GPCRs, a template-based sampling method employing loop structure templates selected from the structure database was developed...
May 22, 2018: Journal of Chemical Information and Modeling
Yu-Jiao Yang, Shuai Wang, Biao Zhang, Hong-Bin Shen
As a relatively new technology to solve the 3D structure of a protein or protein complex, the Single-Particle Reconstruction (SPR) of cryo-EM images shows much superiority and is in a rapidly developing stage. Resolution measurement in SPR, which evaluates the quality of a reconstructed 3D density map, plays a critical role for promoting methodology development of SPR and structural biology. Due to there is no benchmark map in a new structure generation, how to realize the resolution estimation of a new map is still an open problem...
May 21, 2018: Journal of Chemical Information and Modeling
Man Cao, Guodong Chen, Lina Wang, Pingping Wen, Shaoping Shi
The tyrosine residue has been identified as suffering three major post-translational modifications (PTMs) including nitration, sulfation, and phosphorylation, which could be involved in different physiological and pathological processes. Multiple tyrosine residues of the whole protein may be modified concurrently, where PTM of a single tyrosine may affect modification of other neighboring tyrosine residues. Hence, it is significant and beneficial to predict nitration, sulfation, and phosphorylation of tyrosine residues in the whole protein sequence...
May 18, 2018: Journal of Chemical Information and Modeling
Vishal Babu Siramshetty, Qiaofeng Chen, Prashanth Devarakonda, Robert Preissner
Drug-induced inhibition of the human Ether-à-go-go-Related Gene (hERG) encoded potassium ion (K+) channels can lead to fatal cardiotoxicity. Several marketed drugs and promising drug candidates were recalled due to this concern. Diverse modeling methods ranging from molecular similarity assessment to quantitative structure activity relationship analysis employing machine learning techniques have been applied to datasets of varying size and composition (number of blockers and non-blockers). In this study, we highlight the challenges involved in development of a robust classifier for predicting the hERG endpoint using bioactivity data extracted from the public domain...
May 17, 2018: Journal of Chemical Information and Modeling
Andrew Dalke, Jérôme Hert, Christian Kramer
Matched molecular pair analysis (MMPA) enables the automated and systematic compilation of medicinal chemistry rules from compound/property data sets. Here we present mmpdb, an open-source matched molecular pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large data sets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit...
May 17, 2018: Journal of Chemical Information and Modeling
Evgeny Putin, Arip Asadulaev, Yan Ivanenkov, Vladimir Aladinskiy, Benjamin Sánchez-Lengeling, Alán Aspuru-Guzik, Alex Zhavoronkov
In silico modeling is a crucial milestone in modern drug design & development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on generative adversarial network (GAN) paradigm and reinforcement learning (RL)...
May 15, 2018: Journal of Chemical Information and Modeling
Elton Chaves, Itácio Queiroz de Mello Padilha, Demetrius Antônio Machado Araújo, Gerd Bruno Rocha
Ricin is a ribosome-inactivating protein (RIP-type2) consisting of two subunits, Ricin Toxin A (RTA) and Ricin Toxin B (RTB). Due to its cytotoxicity, ricin has worried world authorities for its potential use as a chemical weapon; therefore, its inhibition is of great biotechnological interest. RTA is the target for inhibitor synthesis and pterin derivatives are promising candidates to inhibit it. In this study, we used a combination of molecular docking approach and fast steered molecular dynamics in order to assess the correlation between non-equilibrium work, 〈W〉, and IC50 of six RTA inhibitors...
May 11, 2018: Journal of Chemical Information and Modeling
Alessandro Pedretti, Angelica Mazzolari, Giulio Vistoli
The manuscript describes WarpEngine, a novel platform implemented within the VEGA ZZ suite of software for performing distributed simulations both in local and wide area networks. Despite being tailored for structure-based virtual screening campaigns, WarpEngine possesses the required flexibility to carry out distributed calculations utilizing various pieces of software, which can be easily encapsulated within this platform without changing their source codes. WarpEngine takes advantages of all cheminformatics features implemented in the VEGA ZZ program as well as of its largely customizable scripting architecture thus allowing an efficient distribution of various time-demanding simulations...
May 10, 2018: Journal of Chemical Information and Modeling
Siyang Tian, Yannick Djoumbou, Russ Greiner, David S Wishart
In silico metabolism prediction requires first predicting whether a specific molecule will interact with one or more specific metabolizing enzymes, then predicting the result of each enzymatic reaction. Here, we provide a computational tool, CypReact, for performing this first task of reactant prediction. Specically, CypReact takes as input an arbitrary molecule (specied as a SMILES string or a standard SDF file), and any one of the nine of the most important human cytochrome P450 (CYP450) enzymes -- CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1 or CYP3A4 -- and accurately predicts whether the query molecule will react with that given CYP450 enzyme...
May 8, 2018: Journal of Chemical Information and Modeling
Pei Zhou, Botong Li, Yumeng Yan, Bowen Jin, Libang Wang, Sheng-You Huang
Given the importance of peptide-mediated protein interactions in cellular processes, protein- peptide docking has received increasing attention. Here, we have developed a Hierarchical flexible Peptide Docking approach through fast generation and ensemble docking of peptide conformations, which is referred to as HPepDock. Tested on the LEADS-PEP benchmark data set of 53 diverse complexes with peptides of 3 to 12 residues, HpepDock performed significantly better than the 11 docking protocols of five small-molecule docking programs (DOCK, AutoDock, AutoDock Vina, Surflex, and GOLD) in predicting near-native binding conformations...
May 8, 2018: Journal of Chemical Information and Modeling
Yingchun Cai, Hongbin Yang, Weihua Li, Guixia Liu, Philip W Lee, Yun Tang
Drug metabolism is a complex procedure in human body, involving a series of enzymatically catalyzed reactions. However, it is costly and time-consuming to investigate drug metabolism experimentally, computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multi-classification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building...
May 7, 2018: Journal of Chemical Information and Modeling
Amy K Smith, Dmitri K Klimov
Abeta25-35 is a short, cytotoxic, and naturally-occurring fragment of the Alzheimer's Abeta peptide. To map the molecular mechanism of Abeta25-35 binding to the zwitterionic DMPC bilayer, we have performed replica exchange with solute tempering molecular dynamics simulations using all-atom explicit membrane and water models. Consequences of sequence truncation on the binding mechanism have been measured by utilizing as a control our previous simulations probing binding of the longer peptide Abeta10-40 to the same bilayer...
May 4, 2018: Journal of Chemical Information and Modeling
Kira A Armacost
This perspective describes the transition from academic training (specifically graduate school and a postdoctoral fellowship) to a career in a pharmaceutical industry as a computational chemist. My personal journey from childhood to senior scientist is described, along with suggestions and insights into a career in the pharmaceutical industry.
May 4, 2018: Journal of Chemical Information and Modeling
Anna Theresa Cavasin, Alexander Hillisch, Felix Uellendahl, Sebastian Schneckener, Andreas H Göller
Prediction of compound properties from structure via QSAR and machine-learning approaches is an important computational chemistry task in small molecule drug research. Though many such properties are dependent on 3D structures or even conformer ensembles, the majority of models are based on descriptors derived from 2D structure. Here, we present results from a thorough benchmark study of force field, semiempirical and density functional methods for the calculation of conformer energies in gas-phase and water solvation as a foundation for the correct identification of relevant low-energy conformers...
May 2, 2018: Journal of Chemical Information and Modeling
Boris Vishnepolsky, Andrei Gabrielian, Alex Rosenthal, Darrell E Hurt, Michael Tartakovsky, Grigol Managadze, Maya Grigolava, George I Makhatadze, Malak Pirtskhalava
Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods trying to predict AMP. Most of them can only predict if peptide is showing any antimicrobial potency, but to the best of our knowledge, there are not tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMP being active against particular gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm...
May 2, 2018: Journal of Chemical Information and Modeling
Chuipu Cai, Jiansong Fang, Pengfei Guo, Qi Wang, Huixiao Hong, Javid Moslehi, Feixiong Cheng
Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on use of multitudinous post-marketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and post-marketing surveillance. Specifically,we collected drug-induced cardiovascular complications covering five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB...
April 30, 2018: Journal of Chemical Information and Modeling
Sorin Avram, Alina Bora, Liliana Halip, Ramona Curpan
Protein kinases form a consistent class of promising drug targets, and several efforts have been made to predict the activity of small-molecules against a representative part of the kinome. This study continues our previous work (Bora, A.; Avram, S.; Ciucanu, I.; Raica, M.; Avram, S., Predictive Models for Fast and Effective Profiling of Kinase Inhibitors. J. Chem. Inf. MODEL: 2016, 56, 895-905;, aiming to build and measure the performance of ligand-based kinase inhibitor prediction models...
April 30, 2018: Journal of Chemical Information and Modeling
Fredrik Svensson, Natalia Aniceto, Ulf Norinder, Isidro Cortes-Ciriano, Ola Spjuth, Lars Carlsson, Andreas Bender
Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the outputted prediction intervals to create as efficient (i...
April 27, 2018: Journal of Chemical Information and Modeling
Jie Xia, Terry-Elinor Reid, Song Wu, Liangren Zhang, Xiang Simon Wang
Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approaches including both structure- and ligand-based VS, somewhat hinders modern drug discovery efforts...
April 26, 2018: Journal of Chemical Information and Modeling
Izhar Wallach, Abraham Heifets
Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems that accounts for the similarity amongst inactive molecules as well as active. We investigated seven widely-used benchmarks for virtual screening and classification, and show that the amount of AVE bias strongly correlates with the performance of ligand-based predictive methods irrespective of the predicted property, chemical fingerprint, similarity measure, or previously-applied unbiasing techniques...
April 26, 2018: Journal of Chemical Information and Modeling
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