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Molecular Informatics

Shana V Stoddard, Xavier A May, Fatima Rivas, Kyra Dodson, Sajith Vijayan, Swetha Adhika, Kordarius Parker, Davita L Watkins
Histone Deacetylases (HDACs) are an important family of 18 isozymes, which are being pursued as drug targets for many types of disorders. HDAC2 and HDAC8 are two of the isozymes, which have been identified as drug targets for the design of anti-cancer, neurodegenerative, immunological, and anti-parasitic agents. Design of potent HDAC2 and HDAC8 inhibitors will be useful for the therapeutic advances in many disorders. This work was undertaken to develop potent HDAC2 and HDAC8 inhibitors. A docking study was performed comparing panobinostat derivatives in both HDAC2 and HDAC8...
October 22, 2018: Molecular Informatics
Mariia Matveieva, Mark T D Cronin, Pavel Polishchuk
The study focused on QSAR model interpretation. The goal was to develop a workflow for the identification of molecular fragments in different contexts important for the property modelled. Using a previously established approach - Structural and physicochemical interpretation of QSAR models (SPCI) - fragment contributions were calculated and their relative influence on the compounds' properties characterised. Analysis of the distributions of these contributions using Gaussian mixture modelling was performed to identify groups of compounds (clusters) comprising the same fragment, where these fragments had substantially different contributions to the property studied...
October 22, 2018: Molecular Informatics
Chatchakorn Eurtivong, Jóhannes Reynisson
1880 known drugs were collected and analysed for their mainstream molecular descriptors: MW, log P, HA, HD, RB and PSA. The statistical distributions were fitted to Gaussian functions for each of the descriptors. This gave a mathematical tool to calculate a weighted score, or an Index, for each descriptor. Known Drug Indexes (KDIs) were derived either by summation or multiplication of the Indexes, giving one number for each molecule calculated. The KDI summation and multiplication methods give a theoretical maxima of 6 and 1 respectively...
October 22, 2018: Molecular Informatics
Shan Tang, Na Zhang, Yue Zhou, Wilian A Cortopassi, Matthew P Jacobson, Li-Jiao Zhao, Ru-Gang Zhong
Protein kinase CK2 is considered as an emerging target in cancer therapy, and recent efforts have been made to develop its ATP-competitive inhibitors, but achieving selectivity with respect to related kinases remains challenging because of the highly conserved ATP-binding pocket of kinases. Non-ATP competitive inhibitors might solve this challenge; one such strategy is to identify compounds that target the CK2α/CK2β interface as CK2 holoenzyme antagonists. Here we improved the binding affinity to CK2α and cell-based anti-cancer activity of a CK2β-derived cyclic peptide (Pc) by combining structure-based computational design with experimental evaluation...
October 11, 2018: Molecular Informatics
Hiromasa Kaneko
This paper introduces two generative topographic mapping (GTM) methods that can be used for data visualization, regression analysis, inverse analysis, and the determination of applicability domains (ADs). In GTM-multiple linear regression (GTM-MLR), the prior probability distribution of the descriptors or explanatory variables (X) is calculated with GTM, and the posterior probability distribution of the property/activity or objective variable (y) given X is calculated with MLR; inverse analysis is then performed using the product rule and Bayes' theorem...
September 27, 2018: Molecular Informatics
Jonathan Cardoso-Silva, George Papadatos, Lazaros G Papageorgiou, Sophia Tsoka
Quantitative Structure-Activity Relationship (QSAR) models have been successfully applied to lead optimisation, virtual screening and other areas of drug discovery over the years. Recent studies, however, have focused on the development of models that are predictive but often not interpretable. In this article, we propose the application of a piecewise linear regression algorithm, OPLRAreg, to develop both predictive and interpretable QSAR models. The algorithm determines a feature to best separate the data into regions and identifies linear equations to predict the outcome variable in each region...
September 24, 2018: Molecular Informatics
Tsuyoshi Esaki, Reiko Watanabe, Hitoshi Kawashima, Rikiya Ohashi, Yayoi Natsume-Kitatani, Chioko Nagao, Kenji Mizuguchi
A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance (CLint ) measurements from an open database and performed extensive manual curation...
September 24, 2018: Molecular Informatics
Neha Tripathi, Naeem Shaikh, Prasad V Bharatam, Prabha Garg
The enzyme human topoisomerase IIα (hTopoIIα) is an important anticancer drug target. Due to the availability of multiple inhibitor-binding sites in this enzyme, the anti-hTopoII agents possess high chemical diversity. Chemoinformatics methods can be used to identify lead compounds from large databases for hTopoII inhibitory activity and classify them. In this work, we report the use of machine learning methods to develop classification models for the identification of possible anti-hTopoIIα agents and to classify them as catalytic inhibitors vs...
September 14, 2018: Molecular Informatics
Viviana Consonni, Roberto Todeschini, Davide Ballabio, Francesca Grisoni
Quantitative Structure - Activity Relationship (QSAR) models play a central role in medicinal chemistry, toxicology and computer-assisted molecular design, as well as a support for regulatory decisions and animal testing reduction. Thus, assessing their predictive ability becomes an essential step for any prospective application. Many metrics have been proposed to estimate the model predictive ability of QSARs, which have created confusion on how models should be evaluated and properly compared. Recently, we showed that the metric QF32 is particularly well-suited for comparing the external predictivity of different models developed on the same training dataset...
August 24, 2018: Molecular Informatics
Marta Glavatskikh, Timur Madzhidov, Dragos Horvath, Ramil Nugmanov, Timur Gimadiev, Daria Malakhova, Gilles Marcou, Alexandre Varnek
This paper reports SVR (Support Vector Regression) and GTM (Generative Topographic Mapping) modeling of three kinetic properties of cycloaddition reactions: rate constant (logk), activation energy (Ea) and pre-exponential factor (logA). A data set of 1849 reactions, comprising (4+2), (3+2) and (2+2) cycloadditions (CA) were studied in different solvents and at different temperatures. The reactions were encoded by the ISIDA fragment descriptors generated for Condensed Graph of Reaction (CGR). For a given reaction, a CGR condenses structures of all the reactants and products into one single molecular graph, described both by conventional chemical bonds and "dynamical" bonds characterizing chemical transformations...
August 22, 2018: Molecular Informatics
Alexandre Varnek
No abstract text is available yet for this article.
September 2018: Molecular Informatics
Laurent Hoffer, Christophe Muller, Philippe Roche, Xavier Morelli
For several decades, hit identification for drug discovery has been facilitated by developments in both fragment-based and high-throughput screening technologies. However, a major bottleneck in drug discovery projects continues to be the optimization of primary hits from screening campaigns in order to derive lead compounds. Computational chemistry or molecular modeling can play an important role during this hit-to-lead (H2L) stage by both suggesting putative optimizations and decreasing the number of compounds to be experimentally synthesized and evaluated...
September 2018: Molecular Informatics
Marta Glavatskikh, Timur Madzhidov, Igor I Baskin, Dragos Horvath, Ramil Nugmanov, Timur Gimadiev, Gilles Marcou, Alexandre Varnek
Generative Topographic Mapping (GTM) approach was successfully used to visualize, analyze and model the equilibrium constants (KT ) of tautomeric transformations as a function of both structure and experimental conditions. The modeling set contained 695 entries corresponding to 350 unique transformations of 10 tautomeric types, for which KT values were measured in different solvents and at different temperatures. Two types of GTM-based classification models were trained: first, a "structural" approach focused on separating tautomeric classes, irrespective of reaction conditions, then a "general" approach accounting for both structure and conditions...
September 2018: Molecular Informatics
Omer Kaspi, Abraham Yosipof, Hanoch Senderowitz
This work describes the integration of several data mining and machine learning tools for researching Photovoltaic (PV) solar cells libraries into a unified workflow embedded within a GUI-supported Decision Support System (DSS), named PV Analyzer. The analyzer's workflow is composed of several data analysis components including basic statistical and visualization methods as well as an algorithm for building predictive machine learning models. The analyzer allows for the identification of interesting trends within the libraries, not easily observable using simple bi-parametric correlations...
September 2018: Molecular Informatics
Christoph Sotriffer
Covalent ligands have recently regained considerable attention in drug discovery. The rational design of such ligands, however, is still faced with particular challenges, mostly related to the fact that covalent bond formation is a quantum mechanical phenomenon which cannot adequately be handled by the force fields or empirical approaches typically used for noncovalent protein-ligand interactions. Although the necessity for quantum chemical approaches is clear, they cannot yet routinely be applied on large data sets of ligands or for a broader exploration of binding modes in docking calculations...
September 2018: Molecular Informatics
Petra Schneider, Gisbert Schneider
Pharmacological drug actions are often caused by multi-target effects. While most of the currently approved synthetic drugs were designed to interact with a single 'on-target', these chemical agents often interact with additional 'off-targets'. Understanding and rationalizing these multiple interactions will be indispensable for the design of future precision medicines. We employed computational predictions of drug-target interactions to analyze functional drug-drug relationships. 900 approved drugs were represented in terms of their predicted activity fingerprints, considering 1158 potential target activities...
September 2018: Molecular Informatics
Hongming Chen, Thierry Kogej, Ola Engkvist
Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future...
September 2018: Molecular Informatics
Pavel Sidorov, Elisabeth Davioud-Charvet, Gilles Marcou, Dragos Horvath, Alexandre Varnek
This paper presents the effort of collecting and curating a data set of 15461 molecules tested against the malaria parasite, with robust activity and mode of action annotations. The set is compiled from in-house experimental data and the public ChEMBL database subsets. We illustrate the usefulness of the dataset by building QSAR models for antimalarial activity and QSPR models for modes of actions, as well as by the analysis of the chemical space with the Generative Topographic Mapping method. The GTM models perform well in prediction of both activity and mode of actions, on par with the classical SVM methods...
September 2018: Molecular Informatics
Fuqiang Ban, Kush Dalal, Eric LeBlanc, Hélène Morin, Paul S Rennie, Artem Cherkasov
Androgen receptor (AR) is a master regulator of prostate cancer (PCa), and therefore is a pivotal drug target for the treatment of PCa including its castration-resistance form (CRPC). The development of acquired resistance is a major challenge in the use of the current antiandrogens. The recent advancements in inhibiting AR activity with small molecules specifically designed to target areas distinct from the receptor's androgen binding site are carefully discussed. Our new classes of AR inhibitors of AF2 and BF3 functional sites and DBD domains designed using cheminformatics techniques are promising to circumvent various AR-dependent resistance mechanisms...
September 2018: Molecular Informatics
Filip Miljković, Jürgen Bajorath
Kinases are among the most heavily investigated drug targets and inhibition of kinases and kinase-dependent signaling has become a paradigm for therapeutic intervention. Kinase inhibitors and associated activity data have increasing 'big data' character, which presents challenges for computational analysis, but also unprecedented opportunities for learning from compound data and for data-driven medicinal chemistry. Herein, publicly available kinase inhibitor data are evaluated and a number of characteristics are discussed...
September 2018: Molecular Informatics
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