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Machine Learning in Chemoinformatics and Medicinal Chemistry

biomedical data science 1000年第1期5卷 43-65页

作者： Raquel Rodríguez-Pérez Filip Miljković Jürgen bajorath 2Current affiliation: Novartis Institutes for Biomedical Research Novartis Campus Basel Switzerland 1Department of Life Science Informatics B-IT (Bonn-Aachen International Center for Information Technology) Chemical Biology and Medicinal Chemistry Program Unit LIMES (Life and Medical Sciences Institute) Rheinische Friedrich-Wilhelms-Universität Bonn Germany email: bajorath@bit.uni-bonn.de 3Current affiliation: Data Science and AI Imaging and Data Analytics Clinical Pharmacology and Safety Sciences R&D AstraZeneca Gothenburg Sweden

In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains.

关键词： machine learning deep learning chemoinformatics medicinal chemistry data structures learning strategies

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MultiCalib4DEb: A toolbox exploiting multimodal optimisation in Dynamic Energy budget parameters calibration

arXiv

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arXiv 2023年

作者： Robles, Juan Francisco Chica, Manuel Filgueira, Ramón Agüera, Antonio Damas, Sergio Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Benthic resources Norwegian Institute of Marine Research BergenNO-5817 Norway Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Marine Affairs Program Life Sciences Centre Dalhousie University 1459 Oxford Street HalifaxNSB3H 4R2 Canada

1. Calibration is a crucial step for the validation of computational models and a challenging task to accomplish. 2. Dynamic Energy budget (DEb) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEbtool toolbox. Multimodal evolutionary optimisation could provide DEbtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. 3. In this paper we present MultiCalib4DEb, a MATLAb toolbox directly integrated into the existing DEbtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEb models. 4. MultiCalib4DEb adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEbtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEbtool calibration module and to aid DEbtool users to evaluate the performance of the calibration results. © 2023, CC bY-NC-ND.

关键词： Calibration

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Estimating reclamation-induced carbon loss in coastal wetlands using time series GF-1 WVF data: A case study in the Yangtze Estuary 14

Estimating reclamation-induced carbon loss in coastal wetlan...

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Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017

作者： Ai, Jinquan Gao, Zhiqiang Zhang, Chao Gao, Wei Shi, Runhe Song, Debin Xu, Fuxiang Chen, Maosi Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China School of Geographic Sciences East China Normal University Shanghai200241 China Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai264003 China Joint Laboratory for Environmental Remote Sensing and Data Assimilation East China Normal University Shanghai200241 China USDA UV-B Monitoring and Research Program Center of Remote Sensing and Modeling for Agricultural Sustainability Natural Resource Ecology Laboratory Colorado State University Fort CollinsCO United States

ISBN: (纸本)9781510612679

Coastal wetland is a net carbon sink with a high carbon density. However, coastal reclamation directly changes the structure of coastal wetland ecosystem and consequent carbon sink function. The aim of this work was to estimate the reclamation-induced carbon loss in coastal wetlands using time series GF-1 WVF data. For this purpose, GF-1 WVF imageries of 2013 (before reclamation) and 2017 (after reclamation) in the Yangtze Estuary were collected and analyzed combined with field monitoring. Results showed that the converted coastal wetland area occupied up to 61.60% between 2013 and 2017. Carbon estimation indicated that the coastal wetland before reclamation had greater potential contribution to the global warming mitigation than the wetland reclamation to other land cover types. Finally the vulnerability of carbon stores and uncertain analysis with remote sensing technology in coastal wetlands environment were discussed. We emphasized that long-term monitoring of coastal wetlands and its carbon dynamic are urgently needed, because so many uncertain factors exist in short-term monitoring. © 2017 SPIE.

关键词： Image analysis

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MAATrica: a measure for assessing consistency and methods in medicinal and nutraceutical chemistry papers

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EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 2024年 273卷 116522页

作者： Panzarella, Giulia Gallo, Alessandro Coecke, Sandra Querci, Maddalena Ortuso, Francesco Hofmann-Apitius, Martin Veltri, Pierangelo bajorath, Juergen Alcaro, Stefano Magna Graecia Univ Catanzaro Dipartimento Sci Salute Campus Univ S VenutaViale Europa I-88100 Catanzaro Italy European Commiss Joint Res Ctr Ispra VA Italy Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat D-53757 St Augustin Germany Univ Calabria Dipartimento Ingn Informat Modellist Elettron & Si Arcavacata Di Rende CS Italy Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Magna Graecia Univ Catanzaro Net4Science Srl Campus Univ S VenutaViale Europa I-88100 Catanzaro Italy Assoc CRISEA Ctr Ric & Serv Avanzati Innovaz Rurale I-88055 Belcastro CZ Italy

The growing number of scientific papers and document sources underscores the need for methods capable of evaluating the quality of publications. Researchers who are looking for relevant papers for their studies need ways to assess the scientific value of these documents. One approach involves using semantic search engines that can automatically extract important knowledge from the growing body of text. In this study, we introduce a new metric called "MAATrica," which serves as the foundation for an innovative method designed to evaluate research papers. MAATrica offers a new way to analyze and categorize text, focusing on the consistency of research documents in the life sciences, particularly in the fields of medicinal and nutraceutical chemistry. This method utilizes semantic descriptions to cover in silico experiments, as well as in vitro and in vivo essays. Created to aid in evaluation processes like peer review, MAATrica uses toolkits and semantic applications to build the proposed measure, identify scientific entities, and gather information. We have applied MAATrica to roughly 90,000 papers and present our findings here.

关键词： Medicinal chemistry Nutraceuticals Information extraction Text mining Research metrics

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data science in science: Special Issue on data science in the brain sciences

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data science in science 2023年第1期2卷

作者： Carolina Euan Mark b. Fiecas Hernando Ombao David S. Matteson a Department of Mathematics and Statistics Lancaster University b Division of Biostatistics University of Minnesota c Statistics Program King Abdullah University of Science and Technology d Department of Statistics and Data Science Cornell University

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Protocol to explain support vector machine predictions via exact Shapley value computation

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STAR Protocols 2024年第2期5卷 103010页

作者： Mastropietro, Andrea bajorath, Jürgen Deparment of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany Lamarr Institute for Machine Learning and Artificial Intelligence Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany

Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shapley value computation. We detail the application of these algorithms and provide ready-to-use Python scripts and custom code. The final output of the protocol includes quantitative feature analysis and mapping of important features for visualization. For complete details on the use and execution of this protocol, please refer to Feldmann and bajorath¹ and Mastropietro et al.² © 2024 The Author(s)

关键词： bioinformatics Chemistry Computer sciences

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Structural Characteristics of Compounds With Multitarget Activity

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Future Drug Discovery 2021年第2期3卷

作者： Jürgen bajorath 1 Department of Life Science Informatics & Data Science B-IT LIMES Program Unit Chemical Biology & Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 6 BonnD-53115 Germany

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Transcriptome analysis and gene networks in a rare pediatric tumor

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biotechnology Research and Innovation 2019年第2期3卷 252-264页

作者： Michel L. Leite Elio F. Vanin Stephen Iannaccone Nicolau b. da Cunha Sérgio de Alencar Fabricio F. Costa Laboratory of Data Science Genomic Sciences and Biotechnology Program UCB Brasilia SGAN 916 Modulo B Bloco C 70.790-160 Brasília DF Brazil Cancer Biology and Epigenomics Program USA Developmental Biology Ann & Robert H Lurie Children’s Hospital of Chicago Research Center and Northwestern University’s Feinberg School of Medicine 2430 N. Halsted St. Box 220 Chicago IL USA The Polsky Center for Entrepreneurship and Innovation University of Chicago 1452 E 53rd St Chicago IL USA MATTER Chicago 222 W. Merchandise Mart Plaza Suite 12th Floor Chicago IL 60.654 USA

Ependymoma, a rare pediatric tumor originating from ependymal cells located in the lining of ventricular surfaces in the brain, presents great challenges in treatment despite advances in neurosurgical techniques. In order to identify new molecular biomarkers that could improve clinical management and outcomes, we have used RNA-seq to profile the Whole Transcriptome of three ependymoma samples and one normal control brain tissue, producing a total of 2.5 Gigabases of sequencing data. Different protein-coding gene databases were interrogated for known annotated genes to calculate RPKM and Fold Changes (FGs) for each clustered gene. Using this approach, we were able to identify Differentially Expressed Genes (DEGs) in ependymomas. KEGG pathways and Gene Ontology (GO) categories enriched in the DEGs were analyzed using Enrichr, and protein interaction networks were then built using MetaCore™. Thirty differentially expressed protein-coding genes associated with neurogenesis were identified by TaqMan Real-Time PCR, 17 of these showing statistically significant differential expression. based on these results, we have identified IGF-2 as highly over-expressed in ependymomas and that this is due to loss of DNA methylation in its promoter region. In conclusion, we believe that some of these genes, specially IGF-2 , may be of clinical importance, opening new avenues for disease management and new therapies.

关键词： Ependymomas Transcriptome analysis Expression big data analytics Epigenetic regulation Potential biomarkers.

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A holistic exploration of the potentially recoverable redshift information of Stage IV galaxy surveys

arXiv

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arXiv 2024年

作者： Scott, bryan R. Malz, Alex I. Sorba, Robert Northwestern University 1800 Sherman Ave 8th Floor EvanstonIL60201 United States LSST Discovery Alliance Data Science Fellowship Program United States McWilliams Center for Cosmology and Astrophysics Department of Physics Carnegie Mellon University United States Institute for Computational Astrophysics Saint Mary’s University 923 Robie Street HalifaxNSB3H 3C3 Canada

Extragalactic science and cosmology with Stage IV galaxy surveys will rely almost exclusively on redshift measurements derived solely from photometry, which are subject to systematic and statistical uncertainties with numerous analysis choices, such as that of an estimator and prior information and no universal solution. Single-survey photometric redshift estimates ought to be improved by combining data from multiple surveys, with common wisdom asserting that optical data benefits from additional infrared coverage but not from additional ultraviolet coverage. The degree of improvement for either case is not well-characterized and attempts to quantify it necessitate assumptions of a chosen estimator and its prior information. We apply an information theoretic metric of potentially recoverable redshift information to assess the impact of multi-survey photometry combinations without assuming an estimator nor priors in the context of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) in the optical, Roman and Euclid Surveys in the infrared, and Cosmological Advanced Survey Telescope for Optical-UV Research (CASTOR) in the ultraviolet. We conclude that the addition of UV photometry can benefit redshift determination of certain galaxy populations, but that gain is tempered by their decreased chance of meeting detection criteria at higher wavelengths. We explore the spectral energy distribution (SEDs) of galaxies whose potentially recoverable redshift information is most impacted by additional photometry to provide a basis for future evaluation of which science cases may motivate which combinations. The holistic assessment approach we develop here is generic and may be applied to quantify the impact of combining photometric datasets, changing experimental design choices, including observing strategy optimization, and evaluating systematics mitigation procedures. © 2024, CC bY.

关键词： Cosmology

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Improving access to housing and supportive services for runaway and homeless youth: Reducing vulnerability to human trafficking in New York City

A I I E Transactions

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A I I E Transactions 2024年第3期56卷

作者： Yaren bilge Kaya Kayse Lee Maass Geri L. Dimas Renata Konrad Andrew C. Trapp Meredith Dank a Mechanical and Industrial Engineering Northeastern University Boston MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USA c WPI Business School Worcester Polytechnic Institute Worcester MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USAc WPI Business School Worcester Polytechnic Institute Worcester MA USA d New York University New York City NY USA

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth.

关键词： Human trafficking homelessness capacity expansion mixed-integer linear programming homeless shelters

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