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Journal of Cheminformatics

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https://www.readbyqxmd.com/read/30191348/novel-applications-of-machine-learning-in-cheminformatics
#1
EDITORIAL
Ola Spjuth
No abstract text is available yet for this article.
September 6, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30167882/-ms-ready-structures-for-non-targeted-high-resolution-mass-spectrometry-screening-studies
#2
Andrew D McEachran, Kamel Mansouri, Chris Grulke, Emma L Schymanski, Christoph Ruttkies, Antony J Williams
Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query...
August 30, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30159699/the-influence-of-solid-state-information-and-descriptor-selection-on-statistical-models-of-temperature-dependent-aqueous-solubility
#3
Richard L Marchese Robinson, Kevin J Roberts, Elaine B Martin
Predicting the equilibrium solubility of organic, crystalline materials at all relevant temperatures is crucial to the digital design of manufacturing unit operations in the chemical industries. The work reported in our current publication builds upon the limited number of recently published quantitative structure-property relationship studies which modelled the temperature dependence of aqueous solubility. One set of models was built to directly predict temperature dependent solubility, including for materials with no solubility data at any temperature...
August 29, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30136001/machine-learning-for-the-prediction-of-molecular-dipole-moments-obtained-by-density-functional-theory
#4
Florbela Pereira, João Aires-de-Sousa
Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML schemes. A database was used with 10,071 structures, new molecular descriptors were designed and the models were validated with external test sets. Several ML algorithms were screened. Random forest regression models predicted an external test set of 3368 compounds achieving mean absolute error up to 0...
August 22, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30128806/probing-the-chemical-biological-relationship-space-with-the-drug-target-explorer
#5
Robert J Allaway, Salvatore La Rosa, Justin Guinney, Sara J C Gosline
Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing 'hits' with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug-Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets...
August 20, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30128804/ambit-smirks-a-software-module-for-reaction-representation-reaction-search-and-structure-transformation
#6
Nikolay Kochev, Svetlana Avramova, Nina Jeliazkova
Ambit-SMIRKS is an open source software, enabling structure transformation via the SMIRKS language and implemented as an extension of Ambit-SMARTS. As part of the Ambit project it builds on top of The Chemistry Development Kit (The CDK). Ambit-SMIRKS provides the following functionalities: parsing of SMIRKS linear notations into internal reaction (transformation) representations based on The CDK objects, application of the stored reactions against target (reactant) molecules for actual transformation of the target chemical objects, reaction searching, stereo information handling, product post-processing, etc...
August 20, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30120601/predictive-classification-models-and-targets-identification-for-betulin-derivatives-as-leishmania-donovani-inhibitors
#7
Yuezhou Zhang, Henri Xhaard, Leo Ghemtio
Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L...
August 17, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30109435/p2rank-machine-learning-based-tool-for-rapid-and-accurate-prediction-of-ligand-binding-sites-from-protein-structure
#8
Radoslav Krivák, David Hoksza
BACKGROUND: Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers...
August 14, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30105604/annotation-and-detection-of-drug-effects-in-text-for-pharmacovigilance
#9
Paul Thompson, Sophia Daikou, Kenju Ueno, Riza Batista-Navarro, Jun'ichi Tsujii, Sophia Ananiadou
Pharmacovigilance (PV) databases record the benefits and risks of different drugs, as a means to ensure their safe and effective use. Creating and maintaining such resources can be complex, since a particular medication may have divergent effects in different individuals, due to specific patient characteristics and/or interactions with other drugs being administered. Textual information from various sources can provide important evidence to curators of PV databases about the usage and effects of drug targets in different medical subjects...
August 13, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30105533/chemotion-eln-part-2-adaption-of-an-embedded-ketcher-editor-to-advanced-research-applications
#10
Serhii Kotov, Pierre Tremouilhac, Nicole Jung, Stefan Bräse
The Ketcher editor, available as an Open Source software package for drawing chemical structures, has been expanded to include several features that allow storage, management and application of templates, as well as the use of symbols for a planning and processing of solid phase synthesis. In addition, tools for the drawing of coordinative bonds to represent e.g. organometallic compounds were added. The editor has been implemented into an Electronic Lab Notebook (ELN) application which enables the use of the Ketcher editor for advanced operations in chemistry research...
August 13, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30097821/pubchem-chemical-structure-standardization
#11
Volker D Hähnke, Sunghwan Kim, Evan E Bolton
BACKGROUND: PubChem is a chemical information repository, consisting of three primary databases: Substance, Compound, and BioAssay. When individual data contributors submit chemical substance descriptions to Substance, the unique chemical structures are extracted and stored into Compound through an automated process called structure standardization. The present study describes the PubChem standardization approaches and analyzes them for their success rates, reasons that cause structures to be rejected, and modifications applied to structures during the standardization process...
August 10, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30094683/spices-a-particle-based-molecular-structure-line-notation-and-support-library-for-mesoscopic-simulation
#12
Karina van den Broek, Mirco Daniel, Matthias Epple, Hubert Kuhn, Jonas Schaub, Achim Zielesny
Simplified Particle Input ConnEction Specification (SPICES) is a particle-based molecular structure representation derived from straightforward simplifications of the atom-based SMILES line notation. It aims at supporting tedious and error-prone molecular structure definitions for particle-based mesoscopic simulation techniques like Dissipative Particle Dynamics by allowing for an interplay of different molecular encoding levels that range from topological line notations and corresponding particle-graph visualizations to 3D structures with support of their spatial mapping into a simulation box...
August 9, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30066211/hamdb-a-database-of-human-autophagy-modulators-with-specific-pathway-and-disease-information
#13
Ning-Ning Wang, Jie Dong, Lin Zhang, Defang Ouyang, Yan Cheng, Alex F Chen, Ai-Ping Lu, Dong-Sheng Cao
Autophagy is an important homeostatic cellular recycling mechanism responsible for degrading unnecessary or dysfunctional cellular organelles and proteins in all living cells. In addition to its vital homeostatic role, this degradation pathway also involves in various human disorders, including metabolic conditions, neurodegenerative diseases, cancers and infectious diseases. Therefore, the comprehensive understanding of autophagy process, autophagy-related modulators and corresponding pathway and disease information will be of great help for identifying the new autophagy modulators, potential drug candidates, new diagnostic and therapeutic targets...
July 31, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30043127/multi-objective-de-novo-drug-design-with-conditional-graph-generative-model
#14
Yibo Li, Liangren Zhang, Zhenming Liu
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database...
July 24, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/30032331/novel-pharmacological-maps-of-protein-lysine-methyltransferases-key-for-target-deorphanization
#15
Obdulia Rabal, Andrea Castellar, Julen Oyarzabal
Epigenetic therapies are being investigated for the treatment of cancer, cognitive disorders, metabolic alterations and autoinmune diseases. Among the different epigenetic target families, protein lysine methyltransferases (PKMTs), are especially interesting because it is believed that their inhibition may be highly specific at the functional level. Despite its relevance, there are currently known inhibitors against only 10 out of the 50 SET-domain containing members of the PKMT family. Accordingly, the identification of chemical probes for the validation of the therapeutic impact of epigenetic modulation is key...
July 21, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29995272/molecular-generative-model-based-on-conditional-variational-autoencoder-for-de-novo-molecular-design
#16
Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
July 11, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29943160/inferring-potential-small-molecule-mirna-association-based-on-triple-layer-heterogeneous-network
#17
Jia Qu, Xing Chen, Ya-Zhou Sun, Jian-Qiang Li, Zhong Ming
Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule-MiRNA association prediction (TLHNSMMA) to uncover potential SM-miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM-miRNA associations and miRNA-disease associations into a heterogeneous graph...
June 26, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29943074/admetlab-a-platform-for-systematic-admet-evaluation-based-on-a-comprehensively-collected-admet-database
#18
Jie Dong, Ning-Ning Wang, Zhi-Jiang Yao, Lin Zhang, Yan Cheng, Defang Ouyang, Ai-Ping Lu, Dong-Sheng Cao
Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries...
June 26, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29797000/sachem-a-chemical-cartridge-for-high-performance-substructure-search
#19
Miroslav Kratochvíl, Jiří Vondrášek, Jakub Galgonek
BACKGROUND: Structure search is one of the valuable capabilities of small-molecule databases. Fingerprint-based screening methods are usually employed to enhance the search performance by reducing the number of calls to the verification procedure. In substructure search, fingerprints are designed to capture important structural aspects of the molecule to aid the decision about whether the molecule contains a given substructure. Currently available cartridges typically provide acceptable search performance for processing user queries, but do not scale satisfactorily with dataset size...
May 23, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29796778/putting-hands-to-rest-efficient-deep-cnn-rnn-architecture-for-chemical-named-entity-recognition-with-no-hand-crafted-rules
#20
Ilia Korvigo, Maxim Holmatov, Anatolii Zaikovskii, Mikhail Skoblov
Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention...
May 23, 2018: Journal of Cheminformatics
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