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Journal of Chemical Information and Modeling

Shuheng Huang, Duo Zhang, Hu Mei, MuliadiYeremia Kevin, Sujun Qu, Xianchao Pan, Laichun Lu
Recent researches have increasingly suggested that the crucial factors affecting drug potencies are related not only to the thermodynamic properties but also to the kinetic properties. Therefore, in silico prediction of ligand-binding kinetic properties, especially the dissociation rate constant (koff), has aroused more and more attentions. However, there are still a lot of challenges that need to be addressed. In this paper, steered molecular dynamics (SMD) combined with residue-based energy decomposition was employed to predict the dissociation rate constants of 37 HIV-1 protease inhibitors (HIV-1 PIs)...
November 13, 2018: Journal of Chemical Information and Modeling
Jack Hanson, Kuldip Paliwal, Yaoqi Zhou
Recognizing the widespread existence of intrinsically disordered regions in proteins spurred the development of computational techniques for their detection. All existing techniques can be classified into methods relying on single-sequence information and those relying on evolutionary sequence profiles generated from multiple-sequence alignments. The methods based on sequence profiles are, in general, more accurate because the presence or absence of conserved amino acid residues in a protein sequence provides important information on the structural and functional roles of the residues...
November 13, 2018: Journal of Chemical Information and Modeling
Doriana Levré, Chiara Arcisto, Valentina Mercalli, Alberto Massarotti
In the last years, we have investigated the click-chemical space covered by molecules containing the triazole ring, we generated a database of 1,2,3-triazoles called ZINClick, starting from literature-reported alkynes and azides synthesizable in no more than three synthetic steps from commercially available products. This combinatorial database contains millions of 1,4-disubstituted-1,2,3-triazoles that are easily synthesizable. The library is regularly updated and it is freely downloadable (http://www.ZINClick...
November 12, 2018: Journal of Chemical Information and Modeling
Fang-Yu Lin, Alexander D MacKerell
Halogenated ligands can participate in nonbonding interactions with proteins via halogen bond (XB) or halogen-hydrogen bond donor (X-HBD) interactions. In the context of molecular dynamics (MD) simulations, the accuracy of the simulations depends strongly on the force field (FF) used. To assure good reproduction of XB and X-HBD interactions with proteins, we optimized the previously developed additive CHARMM36/CHARMM General force field (CGenFF) and Drude polarizable force field by including atom pair-specific Lennard-Jones parameters for aromatic halogen-protein interactions...
November 12, 2018: Journal of Chemical Information and Modeling
Ruifeng Liu, Hao Wang, Kyle P Glover, Michael Geoffery Feasel, Anders Wallqvist
Deep neural networks (DNNs) are the major drivers of recent progress in artificial intelligence. They have emerged as the machine-learning method of choice in solving image and speech recognition problems, and their potential has raised the expectation of similar breakthroughs in other fields of study. In this work, we compared three machine-learning methods-DNN, random forest (a popular conventional method), and variable nearest neighbor (arguably the simplest method)-in their ability to predict the molecular activities of 21 in vivo and in vitro datasets...
November 9, 2018: Journal of Chemical Information and Modeling
Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, G Uuml Nter Klambauer, Lars Carlsson, Ola Engkvist, Hongming Chen
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It was recently shown that biochemical or cell-based assay results can be compiled into so called high-throughput fingerprints (HTSFPs) as a new type of descriptor describing molecular bioactivity profiles which can be applied in virtual screening, iterative screening, and target deconvolution...
November 9, 2018: Journal of Chemical Information and Modeling
Michael Gonzalez Durruthy, Silvana Manske Nunes, Juliane Ventura Lima, Marcos A Gelesky, Humberto González-Díaz, José M Monserrat, Riccardo Concu, M Natalia D S Cordeiro
Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and non-linear nano-quantitative structure-toxicity relationship-based artificial neural network (ANN-QSTR) models were setup using the fractal dimensions calculated from CNTs as source of structural complex-geometrical information to predict the potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays...
November 9, 2018: Journal of Chemical Information and Modeling
Yen S Low, Vinicius M Alves, Denis Fourches, Alexander Sedykh, Carolina Horta Andrade, Eugene N Muratov, Ivan Rusyn, Alexander Tropsha
Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches...
November 9, 2018: Journal of Chemical Information and Modeling
Yongna Yuan, Zhuangzhuang Zhang, Matthew J L Mills, Rongjing Hu, Ruisheng Zhang
Computational investigations of RNA properties often rely on a molecular mechanical approach to define molecular potential energy. Force fields for RNA typically employ a point charge model of electrostatics, which does not provide a realistic quantum-mechanical picture. In reality, electron distributions around nuclei are not spherically symmetric and are geometry dependent. A multipole expansion method which allows for incorporation of polarizability and anisotropy in a force field is described, and its applicability to modeling the behavior of RNA molecules is investigated...
November 9, 2018: Journal of Chemical Information and Modeling
Thanh-Hoang Nguyen-Vo, Tri Quang Minh Le, Duy Truong Pham, Tri Duc Nguyen, Phuc Hoang Le, An Duc Thien Nguyen, Thanh Duc Nguyen, Thien-Ngan Ngoc Nguyen, Vu Anh Nguyen, Hai Trong Do, Khang Trinh, Hai Trong Duong, Ly Thi Le
Vietnam carries a highly diverse data of traditional medicine, in which various combinations of herbs were widely used as remedies for many types of diseases. Poor hand-writing records and current text-based databases, however, perplex the conventionalizing and evaluating process of the canonical therapeutic effects. In efforts to reorganize the valuable information, we provide VIETHERB database ( for herbs documented in Vietnamese traditional medicines. This database is constructed with temerity to provide users with information of herbs and other side information including metabolites, diseases, morphologies, and geographical locations for each individual species...
November 8, 2018: Journal of Chemical Information and Modeling
Alžběta Türková, Sankalp Jain, Barbara Zdrazil
Hepatocellular organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are important for proper liver function and the regulation of the drug elimination process. Understanding their roles in different conditions of liver toxicity and cancer requires an in-depth investigation of hepatic OATP-ligand interactions and selectivity. However, such studies are impeded by the lack of crystal structures, the promiscuous nature of these transporters, and the limited availability of reliable bioactivity data, which are spread over different data sources in the open domain...
November 8, 2018: Journal of Chemical Information and Modeling
Ruifeng Liu, Anders Wallqvist
Domain applicability (DA) is a concept introduced to gauge the reliability of quantitative structure-activity relationship (QSAR) predictions. A leading DA metric is ensemble variance, which is defined as the variance of predictions by an ensemble of QSAR models. However, this metric fails to identify large prediction errors in melting point data, despite the availability of large training data sets. In this study, we examined the performance of this metric on melting point data and found that, for most molecules, ensemble variance increased as their structural similarity to the training molecules decreased...
November 7, 2018: Journal of Chemical Information and Modeling
Aoxiang Tao, Yuying Huang, Yasuhiro Shinohara, Matthew L Caylor, Srinath Pashikanti, Dong Xu
As abundant and user-friendly as computer-aided drug design (CADD) software may seem, there is still a large underserved population of biomedical researchers around the world, particularly those with no computational training and limited research funding. To address this important need and help scientists overcome barriers that impede them from leveraging CADD in their drug discovery work, we have developed ezCADD, a web-based CADD modeling environment that manifests four simple design concepts: easy, quick, user-friendly, and 2D/3D visualization-enabled...
November 7, 2018: Journal of Chemical Information and Modeling
Suqing Zheng, Wenping Chang, Wenxin Liu, Guang Liang, Yong Xu, Fu Lin
The human gut microbiota (HGM), which are evolutionarily commensal in the human gastrointestinal system, are crucial to our health. However, HGM can be broadly shaped by multifaceted factors such as intake of drugs. About one-quarter of the existing drugs for humans, which are designed to target human cells rather than HGM, can notably alter the composition of HGM. Therefore, the anticommensal effect of human drugs should be avoided to the maximum extent possible in the drug discovery and development process...
November 7, 2018: Journal of Chemical Information and Modeling
Fleur Legrain, Ambroise van Roekeghem, Stefano Curtarolo, Jesús Carrete, Georg K H Madsen, Natalio Mingo
Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF3 reaches a mean absolute error of 0...
November 6, 2018: Journal of Chemical Information and Modeling
Daniel Markthaler, Hamzeh Kraus, Niels Hansen
Relative folding free energies for a series of amide-to-ester mutations in the Pin1-WW domain are calculated using molecular dynamics simulations. Special focus is given to the identification and elimination of a simulation-related bias which was observed in previous work (Eichenberger et al. Biochim. Biophys. Acta 2015, 1850, 983) by comparing simulation results obtained with two different starting structures. Subtle local variations in the protein starting structure may lead to substantial deviations in the calculated free-energy changes as a consequence of differences in the sampled ϕ/ψ-dihedral angle distributions of the mutated residue...
November 5, 2018: Journal of Chemical Information and Modeling
Mattia Bernetti, Elena Rosini, Luca Mollica, Matteo Masetti, Loredano Pollegioni, Maurizio Recanatini, Andrea Cavalli
Traditionally, a drug potency is expressed in terms of thermodynamic quantities, mostly Kd, and empirical IC50 values. Although binding affinity as an estimate of drug activity remains relevant, it is increasingly clear that it is also important to include (un)binding kinetic parameters in the characterization of potential drug-like molecules. Herein, we used standard in silico screening to identify a series of structurally related inhibitors of hDAAO, a flavoprotein involved in schizophrenia and neuropathic pain...
November 5, 2018: Journal of Chemical Information and Modeling
Flávia Camila Vieira da Silva, Viviane Veiga do Nascimento, Olga Lima Tavares Machado, Lidia da Silva Pereira, Valdirene Gomes, Andre de Oliveira Carvalho
We had previously characterized the inhibitory activity of human salivary α-amylase (HSA) and Callosobruchus maculatus intestinal α-amylases by the plant lipid transfer protein from Vigna unguiculata ( Vu-LTP). Herein, we further studied this inhibitory activity. Firstly by an analysis of protein α-amylases inhibitors complexed with α-amylase we find that positively charged amino acids of inhibitors interact with the active site of α-amylases and we knew that Vu-LTP is rich is positively charged amino acid residues...
November 2, 2018: Journal of Chemical Information and Modeling
Antoine Charpentier, David Mignon, Sophie Barbe, Juan Cortes, Thomas Schiex, Thomas Simonson, David Allouche
Computational Protein Design (CPD) aims to predict amino acid sequences that fold to a specific structure and perform a desired function. CPD depends on a rotamer library, an energy function and an algorithm to search the sequence/conformation space. Variable Neighborhood Search (VNS) with Cost function networks is a powerful framework that can provide tight upper bounds on the global minimum energy. We propose a new CPD heuristic based on VNS, where a subset of the solution space is explored (a ``neighborhood''), whose size is gradually increased with a dedicated probabilistic heuristic...
October 31, 2018: Journal of Chemical Information and Modeling
Hiromasa Kaneko
To achieve simultaneous data visualization and clustering, the method of sparse generative topographic mapping (SGTM) is developed by modifying the conventional GTM algorithm. While the weight of each grid point is constant in the original GTM, it becomes a variable in the proposed SGTM, enabling data points to be clustered on two-dimensional maps. The appropriate number of clusters is determined by optimization based on the Bayesian information criterion. Analysis of numerical simulation data sets along with quantitative structure-property relationship and quantitative structure-activity relationship data sets confirmed that the proposed SGTM provides the same degree of visualization performance as the original GTM and clusters data points appropriately...
October 31, 2018: Journal of Chemical Information and Modeling
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