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Journal articles on the topic 'Marie Curie Actions'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Papp, Katalin. "Advancing Research and Training on Ageing, Place and Home." Magyar Gerontológia 15, no. 42 (2023): 143–48. http://dx.doi.org/10.47225/mg/15/42/14324.

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2

Edinger, Medea. "Promovieren über Ländergrenzen hinweg." Nachrichten aus der Chemie 73, no. 5 (2025): 41–42. https://doi.org/10.1002/nadc.20254150129.

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AbstractAcht Promovierende in verschiedenen EU‐Ländern forschen gemeinsam an einem Projekt. Dieses gehört zum Marie‐Skłodowska‐Curie‐Actions‐Programm (Msca) Horizon Europe der Europäischen Kommission.

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Schermer, E., and G. Wilkie. "624 The role of the Marie Curie Actions in Cancer Research." European Journal of Cancer Supplements 8, no. 5 (2010): 159. http://dx.doi.org/10.1016/s1359-6349(10)71425-7.

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Nurzynska, Karolina, Przemyslaw Skurowski, Magdalena Pawlyta, and Krzysztof Cyran. "Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database." Sensors 21, no. 22 (2021): 7687. https://doi.org/10.3390/s21227687.

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Citation: Nurzynska, K.; Skurowski, P.; Pawlyta, M.; Cyran, K. Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database. Sensors 2021, 21, 7687. https://doi.org/10.3390/s21227687

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Bach, Malgorzata, Aleksandra Werner, Magda Mrozik, and Krzysztof Cyran. "A HIERARCHY OF FINITE STATE MACHINES AS A SCENARIO PLAYER IN INTERACTIVE TRAINING OF PILOTS IN FLIGHT SIMULATORS." International Journal of Applied Mathematics and Computer Science 31, no. 4 (2021): 713–27. https://doi.org/10.34768/amcs-2021-0049.

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Petrelli, Alessandra, Berent J. Prakken, Norman D. Rosenblum, and fellows EUtrain. "Developing translational medicine professionals: the Marie Skłodowska-Curie action model." Journal of Translational Medicine 14, no. 1 (2016): 329. https://doi.org/10.1186/s12967-016-1088-1.

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End goal of translational medicine is to combine disciplines and expertise to eventually promote improvement of the global healthcare system by delivering effective therapies to individuals and society. Well-trained experts of the translational medicine process endowed with profound knowledge of biomedical technology, ethical and clinical issues, as well as leadership and teamwork abilities are essential for the effective development of tangible therapeutic products for patients. In this article we focus on education and, in particular, we discuss how programs providing training on the broad s

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Meyer, Dagmar M. "Junge WissenschaftlerInnen müssen mobil sein." ver.di Report biwifo, no. 4/2004 (October 1, 2004): 7. https://doi.org/10.5281/zenodo.22391.

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Aus der Einleitung: Wer eine Karriere in der Wissenschaft machen will, muss heute international mobil sein. Dabei sind es längst nicht mehr nur die Eliteuniversitäten diesseits und jenseits des Atlantiks, die für NachwuchsforscherInnen besonders attraktiv sind. Auch manch unbekanntere Institution hat exzellente Forschung und Ausbildung zu bieten.

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Ruiz-Eugenio, Laura. "Actuaciones Integradas para la Superación de la Pobreza en Escocia: Madres Adolescentes, Familias Monoparentales y Mujeres Musulmanas." Multidisciplinary Journal of Educational Research 6, no. 3 (2016): 208. http://dx.doi.org/10.17583/remie.2016.2165.

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A literature review on integrative actions aimed at improving living conditions and access through education in areas such as health, employment and social and political participation among vulnerable groups is presented. Particularly, integrative actions targeted to teenage mothers, single families and Muslim women in the Scottish context. This review is part of the research results of Socio-educational action to Overcome poverty and inequalities (2012-2014) funded by Beatriu de Pinós plan for post-doctoral research of the Generalitat de Catalunya [The Governtmnet of Catalonia], co-funded in

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De Waele, Ilse, David Wizel, Livia Puljak, and Zvonimir Koporc. "Ethics appraisal procedure in 79,670 Marie Skłodowska-Curie proposals from the entire European HORIZON 2020 research and innovation program (2014–2020): A retrospective analysis." PLOS ONE 16, no. 11 (2021): e0259582. http://dx.doi.org/10.1371/journal.pone.0259582.

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Introduction Horizon 2020 was the most significant EU Research and Innovation programme ever implemented and included the Marie Skłodowska-Curie Actions (MSCA). Proposals submitted to the MSCA actions awere subject to the Ethics Appraisal Procedure. In this work we explored the ethics appraisal procedure in MSCA H2020. Methods Using a retrospective analysis of pooled anonymized data, we explored the ethics appraisal procedure on proposals submitted to Marie Skłodowska-Curie Actions (MSCA) during the entire Horizon 2020 program period (N = 79,670). Results Our results showed that one of the mos

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Alves, M., Diego-Garcia L. de, G. Vegliante, et al. "P2X7R antagonism suppresses long-lasting brain hyperexcitability following traumatic brain injury in mice." Theranostics 15, no. 4 (2025): 1399–419. https://doi.org/10.7150/thno.97254.

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This work was supported by funding from Science Foundation Ireland (17/CDA/4708, and co-funded under the European Regional Development Fund and by FutureNeuro industry partners 16/RC/3948, 21/RC/10294 and 21/RC/10294_P2), from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (No. 766124), from the H2020 Marie Skłodowska-Curie Actions Individual Fellowship (No. 101032321, 753527, 884956, 796600 and 840262), from the Irish Research Council (Government of Ireland Postdoctoral Fellowship Programme, GOIPD/2020/865 and GOIPD/2020/80

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Baumert, Philipp, Francesco Cenni, and Mikhail L. Antonkine. "Ten simple rules for a successful EU Marie Skłodowska-Curie Actions Postdoctoral (MSCA) fellowship application." PLOS Computational Biology 18, no. 8 (2022): e1010371. http://dx.doi.org/10.1371/journal.pcbi.1010371.

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Pina, David G., Darko Hren, and Ana Marušić. "Peer Review Evaluation Process of Marie Curie Actions under EU’s Seventh Framework Programme for Research." PLOS ONE 10, no. 6 (2015): e0130753. http://dx.doi.org/10.1371/journal.pone.0130753.

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Vidu, Ana. "From Bystanders to Upstanders: Supporters and Key Informants for Victims of Gender Violence." International Journal of Environmental Research and Public Health 19, no. 14 (2022): 8521. https://doi.org/10.3390/ijerph19148521.

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Scientific literature has presented relevant evidence about the existence of gender violence in science and has evaluated some programs and actions against this problem. Although many researchers have identified the importance of those intervention programs to overcome this harassment, it is still a predominant reality in institutions, surrounded by the law of silence. Emerging lines of research are studying which of those programs are successful in this endeavor, and their transferability to other contexts. This research has analyzed one program: Programme of Women's Dialogic Action (ProWomen

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Buljan, Ivan, David G. Pina, Antonija Mijatović, and Ana Marušić. "Are numerical scores important for grant assessment? A cross-sectional study." F1000Research 12 (September 5, 2024): 1216. http://dx.doi.org/10.12688/f1000research.139743.2.

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Background: In the evaluation of research proposals, reviewers are often required to provide their opinions using various forms of quantitative and qualitative criteria. In 2020, the European Commission removed, for the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) funding scheme, the numerical scores from the individual evaluations but retained them in the consensus report. This study aimed to assess whether there were any differences in reviewer comments’ linguistic characteristics after the numerical scoring was removed, compared to comments from 2019 when numeric

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Buljan, Ivan, David G. Pina, Antonija Mijatović, and Ana Marušić. "Are numerical scores important for grant proposals' evaluation? A cross sectional study." F1000Research 12 (September 26, 2023): 1216. http://dx.doi.org/10.12688/f1000research.139743.1.

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Background: In the evaluation of research proposals, reviewers are often required to provide their opinions using various forms of quantitative and qualitative criteria. In 2020, the European Commission removed, for the Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN) funding scheme, the numerical scores from the individual evaluations but retained them in the consensus report. This study aimed to assess whether there were any differences in reviewer comments’ linguistic characteristics after the numerical scoring was removed, compared to comments from 2019 when numeric

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Cowley, Emma S., Theoni Massara, Ciara Wilton, et al. "Ten lessons for future MSCA/COFUND applicants." Open Research Europe 4 (October 25, 2024): 233. http://dx.doi.org/10.12688/openreseurope.18597.1.

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The Marie Skłodowska-Curie Actions (MSCA) support researchers' training and development and prioritise international mobility and interdisciplinary collaboration. As the European Commission’s flagship doctorate and post-doctorate fellowship programme, it is fiercely competitive. This perspective piece aims to support future applicants of the programme by presenting ten lessons learned derived from three MSCA/COFUND projects: DTA3 doctoral programme in the UK, Train@Ed post-doctoral programme at the University of Edinburgh, and iMED at the University of Copenhagen, Denmark. The lessons cover re

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Bandelli, Daniela. "Feminism and gestational surrogacy. Theoretical reconsiderations in the name of the child and the woman." Italian Sociological Review 9, no. 3 (2019): 345–61. https://doi.org/10.13136/isr.v9i3.297.

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This article discusses the limits of the feminist theory of commodification of women’s body in contrasting the diffusion of gestational surrogacy as a new reproductive practice and women’s labour. Feminist scholars and activists argue that surrogacy needs to be banned to protect the unitarity of motherhood and the dignity of women. In assuming that surrogates are victims of unbalanced power relations this theoretical perspective obscures women’s decision-making process to engage in free actions, which can be nevertheless detrimental to the female subject’s and other peo

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Parrinello, S., F. Picchio, R. De Marco, and A. Dell’Amico. "DOCUMENTING THE CULTURAL HERITAGE ROUTES. THE CREATION OF INFORMATIVE MODELS OF HISTORICAL RUSSIAN CHURCHES ON UPPER KAMA REGION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 23, 2019): 887–94. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-887-2019.

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<p><strong>Abstract.</strong> The present paper illustrates the documentation activities developed since 2013 on Upper Kama territories, preliminary to an extensive and joint research action within the European project “PROMETHEUS” (2019&amp;ndash;2021), which aims to produce digitized databases and models for the management of the main religious monuments present on this Russian area, nowadays endangered by risk of conservation. The project is funded by the EU program Horizon 2020 &amp;ndash; R&amp;I &amp;ndash; RISE &amp;ndash; Research &amp; Innovat

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Puigvert, Lidia. "Female University Students Respond to Gender Violence through Dialogic Feminist Gatherings." International and Multidisciplinary Journal of Social Sciences 5, no. 2 (2016): 183. http://dx.doi.org/10.17583/rimcis.2016.2118.

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Within the framework of the "Free_Teen_Desire" research project led by the University of Cambridge and funded by the programme Marie Curie Actions[i], a survey was conducted. Vignette-Test data for 127 female university students (ages 18-27 years) in Spain reveals that the wish to hook up with a violent young man significantly decreases after a gathering on the topic of the Mirage of Upward Mobility, a successful programme elaborated in Dialogic Feminism (Butler, Beck & Puigvert, 2003). In the pre-test,Â 78.4%Â of the respondents stated that their female friends wouldÂ likeÂ to hook up wi

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20

Voronin, BA, and AV Anikeev. "Analyzing Work from Graduates of the Marie Curie Alumni Association as a tool for EU Collaboration with National Scientific Expatriate Communities." Science Governance and Scientometrics 14, no. 4 (2019): 504–22. https://doi.org/10.33873/2686-6706.2019.14-4.504-522.

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<strong>Introduction.</strong> One of the key distinctive features of modern science is its global nature, which has resulted in high levels of transnational academic mobility and international competition for talent among countries and universities. Programs that support academic mobility and alumni associations play an important role among the primary engines of that competitiveness. The study reviews the Marie Skłodowska-Curie Actions (MSCA) and Marie Curie Alumni Association (MCAA) as instruments of the European Union for cooperation with national scientific expatr

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Heyard, Rachel, David G. Pina, Ivan Buljan, and Ana Marušić. "Assessing the potential of a Bayesian ranking as an alternative to consensus meetings for decision making in research funding: A case study of Marie Skłodowska-Curie actions." PLOS ONE 20, no. 3 (2025): e0317772. https://doi.org/10.1371/journal.pone.0317772.

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Funding agencies rely on panel or consensus meetings to summarise individual evaluations of grant proposals into a final ranking. However, previous research has shown inconsistency in decisions and inefficiency of consensus meetings. Using data from the Marie Skłodowska-Curie Actions, we aimed at investigating the differences between an algorithmic approach to summarise the information from grant proposal individual evaluations to decisions after consensus meetings, and we present an exploratory comparative analysis. The algorithmic approach employed was a Bayesian hierarchical model resulting

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Biswas, Roshni, Axel Schiller, Chiara Casolani, et al. "Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences." Open Research Europe 1 (April 7, 2021): 34. http://dx.doi.org/10.12688/openreseurope.13094.1.

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Background: The Marie-Skłodowska-Curie Actions’ (MSCA) Innovative Training Network (ITN) is a doctoral training programme jointly implemented by academic institutions and industries from countries across Europe and beyond. To our knowledge no study has examined the experience of students participating in MSCA-ITNs. This study aims to evaluate and report MSCA-ITN Early Stage Researcher (ESR) experiences. Methods: The Innovative Training Network - Evaluation Questionnaire (ITN-EQ) was developed to assess supervision, training, collaborations and experiences of ESRs and forwarded to two tinnitus-

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Mentesana, Roberta, Anno Hein, Marisol Madrid i Fernàndez, Vassilis Kilikoglou, and Jaume Buxeda i Garrigós. "Think Globally, Act Locally: Global Requirements and Local Transformation in Sugar Pots Manufacture in Sicily in the Medieval and Post-Medieval Periods." Minerals 12, no. 4 (2022): 423. http://dx.doi.org/10.3390/min12040423.

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Since medieval times, sugar production and consumption has had a huge impact on European social, cultural, and economic development. The introduction of sugar cultivation entailed knowledge transfer and new technological requirements, such as the manufacture of sugar pots used to crystallise sugar, which requires a specific design, and thermal and mechanical properties. This paper presents part of the results of the SPotEU project, funded under the Marie Skłodowska-Curie Actions, which explores the development and impact of sugar production in western Europe through the study of sugar pot manu

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Domínguez-Amorós, Màrius, and Patricio Solís. "La desigualdad social en Europa y América Latina. Dimensiones relevantes de la investigación comparativa." Revista Española de Sociología 30, no. 3 (2021): a55. http://dx.doi.org/10.22325/fes/res.2021.55.

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La mayoría de los artículos recogidos en este número monográfico son el resultado de diversas investigaciones y trabajos de colaboración realizados en el marco del proyecto europeo International Network for Comparative Analysis of Social Inequalities (INCASI). Los autores del monográfico forman parte de una red de veinte universidades de cinco países europeos (España, Italia, Francia, Reino Unido y Finlandia) y de cinco países latinoamericanos (Argentina, Uruguay, Chile, Brasil y México). La red se dedica a la investigación comparativa de las desigualdades sociales desarrollando un proyecto eu

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Hadjixenophontos, Efi, Erika Michela Dematteis, Nicola Berti, et al. "A Review of the MSCA ITN ECOSTORE—Novel Complex Metal Hydrides for Efficient and Compact Storage of Renewable Energy as Hydrogen and Electricity." Inorganics 8, no. 3 (2020): 17. http://dx.doi.org/10.3390/inorganics8030017.

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Hydrogen as an energy carrier is very versatile in energy storage applications. Developments in novel, sustainable technologies towards a CO2-free society are needed and the exploration of all-solid-state batteries (ASSBs) as well as solid-state hydrogen storage applications based on metal hydrides can provide solutions for such technologies. However, there are still many technical challenges for both hydrogen storage material and ASSBs related to designing low-cost materials with low-environmental impact. The current materials considered for all-solid-state batteries should have high conducti

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Tetko, Igor, Ola Engkvist, Uwe Koch, Jean-Louis Reymond, and Hongming Chen. "BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry." July 28, 2016. https://doi.org/10.1002/m inf.201600073.

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The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectat

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Tetko, Igor, Ola Engkvist, and Hongming Chen. "Does 'Big Data' exist in medicinal chemistry, and if so, how can it be harnessed?" September 15, 2016. https://doi.org/10.4155/fmc-2016-0163.

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The term ‘Big Data’ has gained increasing popularity within the chemistry field and across science broadly in recent years. Chemical databases have seen a dramatic growth over the past decade, with, for example, ChEMBL, REAXYS and PubChem providing hundreds of millions of experimental facts for tens of millions of compounds. Moreover, even larger datasets of experimental measurements are held within in-house data collections at pharma companies. Overall, the total number of entries across these databases is in the range of a billion, 10<sup>9</sup>; however, although this number ma

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Rodríguez-Pérez, Raquel, Martin Vogt, and Jürgen Bajorath. "Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds." April 4, 2017. https://doi.org/10.1021/acs.jcim.7b00088.

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Support vector machine (SVM) modeling is one of the most popular machine learning approaches in chemoinformatics and drug design. The influence of training set composition and size on predictions currently is an underinvestigated issue in SVM modeling. In this study, we have derived SVM classification and ranking models for a variety of compound activity classes under systematic variation of the number of positive and negative training examples. With increasing numbers of negative training compounds, SVM classification calculations became increasingly accurate and stable. However, this was onl

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Rodríguez-Pérez, Raquel, Martin Vogt, and Jürgen Bajorath. "Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction." October 4, 2017. https://doi.org/10.1021/acsomega.7b01079.

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In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In addition, support vector regression (SVR) has become a preferred approach for modeling nonlinear structure−activity relationships and predicting compound potency values. For the closely related SVM and SVR methods, fingerprints (i.e., bit string or feature set representations of chemical structure and properties) are generally preferred descriptors. Herein, we have compared SVM and SVR calcula

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Visini, Ricardo, Josep Arús-Pous, Mahendra Awale, and Jean-Louis Reymond. "Virtual Exploration of the Ring Systems Chemical Universe." October 16, 2017. https://doi.org/10.1021/acs.jcim.7b00457.

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Here, we explore the chemical space of all virtually possible organic molecules focusing on ring systems, which represent the cyclic cores of organic molecules obtained by removing all acyclic bonds and converting all remaining atoms to carbon. This approach circumvents the combinatorial explosion encountered when enumerating the molecules themselves. We report the chemical universe database GDB4c containing 916 130 ring systems up to four saturated or aromatic rings and maximum ring size of 14 atoms and GDB4c3D containing the corresponding 6 555 929 stereoisomers. Almost all (98.6%) of these

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Hongming, Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke. "The rise of deep learning in drug discovery." January 31, 2018. https://doi.org/10.1016/j.drudis.2018.01.039.

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Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples w

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Sosnin, Sergey, Mariia Vashurina, Michael Withnall, Pavel Karpov, Maxim Fedorov, and Igor Tetko. "A Survey of Multi‐task Learning Methods in Chemoinformatics." November 28, 2018. https://doi.org/10.1002/minf.201800108.

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Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi‐learning appro

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Rodríguez-Pérez, Raquel, Tomoyuki Miyao, Swarit Jasial, Martin Vogt, and Jürgen Bajorath. "Prediction of Compound Profiling Matrices Using Machine Learning." April 30, 2018. https://doi.org/10.1021/acsomega.8b00462.

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Screening of compound libraries against panels of targets yields profiling matrices. Such matrices typically contain structurally diverse screening compounds, large numbers of inactives, and small numbers of hits per assay. As such, they represent interesting and challenging test cases for computational screening and activity predictions. In this work, modeling of large compound profiling matrices was attempted that were extracted from publicly available screening data. Different machine learning methods including deep learning were compared and different prediction strategies explored. Predic

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Rodríguez-Pérez, Raquel, and Jürgen Bajorath. "Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data." September 1, 2018. https://doi.org/10.1021/acsomega.8b01682.

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urrently, there is a high level of interest in deep learning and multitask learning in many scientific fields including the life sciences and chemistry. Herein, we investigate the performance of multitask deep neural networks (MT-DNNs) compared to random forest (RF) classification, a standard method in machine learning, in predicting compound profiling experiments. Predictions were carried out on a large profiling matrix extracted from biological screening data. For model building, submatrices with varying data density of 5-100% were generated to investigate the influence of data sparseness on

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Ghosh, Dipan, Uwe Koch, Kamyar Hadian, Michael Sattler, and Igor Tetko. "Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays." April 1, 2018. https://doi.org/10.1021/acs.jcim.7b00574.

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Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such m

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Arús-Pous, Josef, Thomas Blaschke, Silas Ulander, Jean‑Louis Reymond, Hongming Chen, and Ola Engkvist. "Exploring the GDB-13 chemical space using deep generative models." March 12, 2019. https://doi.org/10.1186/s13321-019-0341-z.

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Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1% of the database) reproduces 68.9% of the entire database after training, when sampling 2 billion molecules. We also developed a method to assess the quality of the training process using negative log-likelihood plots. Furthermore, we use a mathematical model based on the "coup

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Lin, Arkadii, Dragos Horvath, Gilles Marcou, Bernd Beck, and Alexandre Varnek. "Multi-task generative topographic mapping in virtual screening." March 1, 2019. https://doi.org/10.1007/s10822-019-00188-x.

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The previously reported procedure to generate "universal" Generative Topographic Maps (GTMs) of the drug-like chemical space is in practice a multi-task learning process, in which both operational GTM parameters (example: map grid size) and hyperparameters (key example: the molecular descriptor space to be used) are being chosen by an evolutionary process in order to fit/select "universal" GTM manifolds. After selection (a one-time task aimed at optimizing the compromise in terms of neighborhood behavior compliance, over a large pool of various biological targets), for any

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Rodríguez-Pérez, Raquel, and Jürgen Bajorath. "Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors." February 27, 2019. https://doi.org/10.1021/acsomega.9b00298.

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Compound activity prediction is a major application of machine learning (ML) in pharmaceutical research. Conventional single-task (ST) learning aims to predict active compounds for a given target. In addition, multitask (MT) learning attempts to simultaneously predict active compounds for multiple targets. The underlying rationale of MT learning is to guide and further improve modeling by exploring and exploiting related prediction tasks. For MT learning, deep neural networks (DNNs) are often used, establishing a link between MT and deep learning. In this work, ST and MT strategies for ML meth

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David, Laurianne, Jarrod Walsh, Noé Sturm, et al. "Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies." September 19, 2019. https://doi.org/10.1002/cmdc.201900395.

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A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usua

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Laufkötter, Oliver, Tomoyuki Miyao, and Jürgen Bajorath. "Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents." September 5, 2019. https://doi.org/10.1021/acsomega.9b02470.

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Similarity searching (SS) is a core approach in computational compound screening and has a long tradition in pharmaceutical research. Over the years, different approaches have been introduced to increase the information content of search calculations and optimize the ability to detect compounds having similar activity. We present a large-scale comparison of distinct search strategies on more than 600 qualifying compound activity classes. Challenging test cases for SS were identified and used to evaluate different ways to further improve search performance, which provided a differentiated view

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Laurianne, David, Josep Arús-Pous, Johan Karlsson, et al. "Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research." November 5, 2019. https://doi.org/10.5281/zenodo.3529160.

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n recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generatio

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Blaschke, Thomas, Filip Miljković, and Jürgen Bajorath. "Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis." November 12, 2019. https://doi.org/10.1021/acsomega.9b00492.

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The ability of compounds to interact with multiple targets is also referred to as promiscuity. Multitarget activity of pharmaceutically relevant compounds provides the foundation of polypharmacology. Promiscuity cliffs (PCs) were introduced as a data structure to identify and organize similar compounds with large differences in promiscuity. Many PCs were obtained on the basis of biological screening data or compound activity data from medicinal chemistry. In this work, PCs were used as a source of different classes of promiscuous and nonpromiscuous compounds with close structural relationships

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Arús-Pous, Josep, Simon Viet Johansson, Oleksii Prykhodko, et al. "Randomized SMILES strings improve the quality of molecular generative models." November 26, 2019. https://doi.org/10.1186/s13321-019-0393-0.

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Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. Herein we perform an extensive benchmark on models trained with subsets of GDB-13 of different sizes (1 million, 10,000 and 1000), with different SMILES variants (canonical, randomized and DeepSMILES), with two different recurrent cell types (LSTM and GRU) and with different hyperparameter combinations. To guide the benchmarks new metrics were developed that define how well a model has gene

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Rodríguez-Pérez, Raquel, and Jürgen Bajorath. "Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values." November 26, 2019. https://doi.org/10.1021/acs.jmedchem.9b01101.

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In qualitative or quantitative studies of structure–activity relationships (SARs), machine learning (ML) models are trained to recognize structural patterns that differentiate between active and inactive compounds. Understanding model decisions is challenging but of critical importance to guide compound design. Moreover, the interpretation of ML results provides an additional level of model validation based on expert knowledge. A number of complex ML approaches, especially deep learning (DL) architectures, have distinctive black-box character. Herein, a locally interpretable explanatory

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Kotsias, Panagiotis-Christos, Josep Arús-Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, and Esben Jannik Bjerrum. "Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)." November 1, 2019. https://doi.org/10.26434/chemrxiv.9860906.v2.

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Deep learning has acquired considerable momentum over the past couple of years in the domain of <em>de-novo</em> drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of

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Withnall, Michael, Edvard Lindelöf, Ola Engkvist, and Hongming Chen. "Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction." September 23, 2019. https://doi.org/10.26434/chemrxiv.9873599.v1.

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Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical–chemical and bioactivity datasets from the literature. We remove the need to intro-duce a priori knowledge of the tas

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Thakkar, Amol, Thierry Kogej, Jean-Louis Reymond, Ola Engkvist, and Esben Jannik Bjerrum. "Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain." November 5, 2019. https://doi.org/10.1039/c9sc04944d.

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Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory Notebooks (ELN), and the publicly available United States Patent Office (USPTO) extracts, are sufficient for the prediction of full synthetic routes to compounds of interest in medicinal chemistry. As such we have assessed the models on 1731 compounds from 41 virtual libraries f

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Yoram, Bar-Zeev. "A guide to the MSCA DN funding scheme." July 27, 2021. https://doi.org/10.5281/zenodo.8052376.

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The Marie Skłodowska-Curie Actions – Doctoral Networks (MSCA DN) provides doctoral candidates with the opportunity to acquire new skills and knowledge, and expand their network. At the same time, MSCA DN improve the quality of doctoral training and R&I capacity of the participating organisations, as well as their interdisciplinary and inter-sectoral cooperation. The objective of MSCA DN is to support doctoral programs carried out by a consortium composed of members from the academic and non-academic sectors. If you are considering applying to this funding scheme – it is essenti

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Rossi, Sergio, and Lucia Rizzo. "The Importance of Food Pulses in Benthic-Pelagic Coupling Processes of Passive Suspension Feeders." April 5, 2021. https://doi.org/10.3390/w13070997.

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Benthic-pelagic coupling processes and the quantity of carbon transferred from the water column to the benthic suspension feeders need multiple intensive sampling approaches where several environmental variables and benthos performance are quantified. Here, activity, dietary composition, and capture rates of three Mediterranean gorgonians (Paramuricea clavata, Eunicella singularis, and Leptogorgia sarmentosa) were assessed in an intensive cycle considering different variables such as the seston concentration and quality (e.g., carbon, nitrogen, and zooplankton), the colony branch patterns, and

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Aru, Silvia. "Abandonment, Agency, Control: Migrants' Camps in Ventimiglia." June 10, 2021. https://doi.org/10.1111/anti.12738.

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AbstractThis article examines the governance mechanisms regulating migrants' presence in Ventimiglia—an Italian city on the north‐west border—and migrants' agency, following the reintroduction of French border controls in 2015. The study looks at different kinds of camps: the formal one run by the Red Cross and the makeshift camps and settlements that sprang up over time. This work contributes to the academic debate on the interplay between migrants and border regimes by arguing two fundamental points. First, migration governance changed the nature of the makeshift settlements, pushing them fr

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