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A systematic review of physical–digital play technology and developmentally relevant child behaviour

International Journal of Child-computer Interaction 2021年 30卷 100323-100323页

作者： Torres, Pablo E. Ulrich, Philip I.N. Cucuiat, Veronica Cukurova, Mutlu Fercovic De la Presa, María Clara Luckin, Rose Carr, Amanda Dylan, Thomas Durrant, Abigail Vines, John Lawson, Shaun Faculty of Education University of Cambridge 184 Hills Road Cambridge UK United Kingdom School of Psychology and Life Sciences Canterbury Christ Church University N Holmes Rd Canterbury UK United Kingdom UCL Knowledge Lab University College London 23-29 Emerald St Holborn London UK United Kingdom School of Design Northumbria University Squires Building 306C City Campus Newcastle upon Tyne United Kingdom Open Lab School of Computing Newcastle University 1 Science Square Newcastle Helix Newcastle upon Tyne United Kingdom School of Informatics The University of Edinburgh 10 Crichton Street Edinburgh United Kingdom Computer and Information Sciences Northumbria University Pandon Building Camden Street Newcastle-upon-Tyne UK United Kingdom

New interactive physical–digital play technologies are shaping the way children play. These technologies refer to digital play technologies that engage children in analogue (non-digital) forms of behaviour, either alone or with others. Current interactive physical–digital play technologies include robots, digital agents, mixed or augmented reality devices, and smart-eye based gaming. Little is known, however, about the ways in which these technologies could promote or damage child development. This systematic review was aimed at understanding if and how these physical–digital play technologies promoted developmentally relevant behaviour (related to transferable skills and physical activity) in typically developing 0 to 12 year-olds. Psychology, Education, and computer science databases were searched producing 635 papers. A total of 31 papers met the inclusion criteria, of which 17 were of high enough quality to be included for synthesis. A theoretical framework was developed to guide our review and a thematic analysis was applied to find patterns across empirical studies. Results indicate that these new interactive play technologies could have a positive effect on children's developmentally relevant behaviour. The review identified specific ways in which different behaviours were promoted by the play interactivity. Providing information about own performance promoted self-monitoring. Slowing interactivity, play interdependency, and joint object accessibility promoted collaboration. Offering delimited choices promoted decision making. Problem solving and physical activity were promoted by requiring children to engage in them to keep playing. Four overarching principles underpinned the ways in which phygital play technologies afforded child behaviour. These included social expectations framing play situations, the directiveness of action regulations (i.e., inviting, guiding or forcing behaviours), the technical features of play technologies (digital play mechanics and

关键词： Child behaviour Child development Child–computer interactions Digital play Systematic review

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Adversarial machine learning

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Synthesis Lectures on Artificial Intelligence and Machine Learning 2018年第3期12卷 1-169页

作者： Vorobeychik, Yevgeniy Kantarcioglu, Murat Vanderbilt University Department of Computer Science Computer Engineering and Biomedical Informatics United States UTD Data Security and Privacy Lab. University of Texas Dallas United States

ISBN: (纸本)9781681733951

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for

关键词： Game theory

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A Digitally Enhanced Ethnography for Craft Action and Process Understanding

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Applied sciences 2025年第10期15卷

作者： Xenophon Zabulis Partarakis Nikolaos Vasiliki Manikaki Ioanna Demeridou Arnaud Dubois Inés Moreno Valentina Bartalesi Nicolò Pratelli Carlo Meghini Sotiris Manitsaris Gavriela Senteri Institute of Computer Science Foundation for Research and Technology—Hellas (FORTH) GR-70013 Heraklion Greece Department of Applied Informatics University of Macedonia 156 Egnatia Street GR-54636 Thessaloniki Greece French National Center for Scientific Research 3 Rue Michel Ange 75016 Paris France Histoire des Technosciences en Société Conservatoire National des Arts et Métiers (HT2S-CNAM) Case 1 LAB10 2 Rue Conté 75003 Paris France Istituto di Scienza e Tecnologie Della Informazione (ISTI) Consiglio Nazionale Delle Ricerche (CNR) Area Della Ricerca CNR 56124 Pisa Italy Centre for Robotics MINES ParisTech PSL Universite 60 Boulevard Saint Michel 75006 Paris France

Traditional ethnographic methods have long been employed to study craft practices, yet they often fall short of capturing the full depth of embodied knowledge, material interactions, and procedural workflows inherent in craftsmanship. This paper introduces a digitally enhanced ethnographic framework that integrates Motion Capture, 3D scanning, audiovisual documentation, and semantic knowledge representation to document both the tangible and dynamic aspects of craft processes. By distinguishing between endurant (tools, materials, objects) and perdurant (actions, events, transformations) entities, we propose a structured methodology for analyzing craft gestures, material behaviors, and production workflows. The study applies this proposed framework to eight European craft traditions—including glassblowing, tapestry weaving, woodcarving, porcelain pottery, marble carving, silversmithing, clay pottery, and textile weaving—demonstrating the adaptability of digital ethnographic tools across disciplines. Through a combination of multimodal data acquisition and expert-driven annotation, we present a comprehensive model for craft documentation that enhances the preservation, education, and analysis of artisanal knowledge. This research contributes to the ongoing evolution of ethnographic methods by bridging digital technology with Cultural Heritage studies, offering a robust framework for understanding the mechanics and meanings of craft practices. © 2025 by the authors.

关键词： Image representation

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Designing Ecosystems of Intelligence from First Principles

arXiv

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arXiv 2022年

作者： Friston, Karl J. Ramstead, Maxwell J.D. Kiefer, Alex B. Tschantz, Alexander Buckley, Christopher L. Albarracin, Mahault Pitliya, Riddhi J. Heins, Conor Klein, Brennan Millidge, Beren Sakthivadivel, Dalton A.R. Smithe, Toby St Clere Koudahl, Magnus Tremblay, Safae Essafi Petersen, Capm Fung, Kaiser Fox, Jason G. Swanson, Steven Mapes, Dan René, Gabriel VERSES AI Research Lab Los AngelesCA United States Wellcome Centre for Human Neuroimaging University College London London United Kingdom Department of Philosophy Monash University Melbourne Australia Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science Université du Québec à Montréal MontréalQC Canada Department of Experimental Psychology University of Oxford Oxford United Kingdom Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Network Science Institute Northeastern University BostonMA United States Brain Network Dynamics Unit University of Oxford Oxford United Kingdom Department of Mathematics Stony Brook University Stony BrookNY United States Department of Physics and Astronomy Stony Brook University Stony BrookNY United States Department of Biomedical Engineering Stony Brook University Stony BrookNY United States Topos Institute BerkeleyCA United States Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Philosophy Université du Québec à Montréal MontréalQC Canada

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call "shared intelligence". This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems;namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology. © 2022, CC BY.

关键词： Ecosystems

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using computer Simulation Technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big Data Earth system emergency geospatial sciences epidemics applications

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Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

arXiv

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arXiv 2021年

作者： Ayoobi, Nooshin Sharifrazi, Danial Alizadehsani, Roohallah Shoeibi, Afshin Gorriz, Juan M. Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Chofreh, Abdoulmohammad Gholamzadeh Goni, Feybi Ariani Klemeš, Jiří Jaromír Mosavi, Amir Department of Mathematics Savitribai Phule Pune University Pune411007 India Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Mathematics Faculty of Science University of Bojnord Iran Sustainable Process Integration Laboratory - SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Brno Technická 2896/2 Brno616 69 Czech Republic Department of Management Faculty of Business and Management Brno University of Technology VUT Brno Kolejní 2906/4 Brno612 00 Czech Republic Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences 1430 Norway

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. Several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organizations working against COVID-19 and determining their long-term plans. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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A computational model for flexibility in emotion regulation

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Procedia computer science 2018年 145卷 572-580页

作者： Nimat Ullah Jan Treur Sander L. Koole Behavioural Informatics Group Department of Computer Science Vrije Universiteit Amsterdam The Netherlands Amsterdam Emotion Regulation Lab Department of Clinical Psychology Vrije Universiteit Amsterdam The Netherlands

Emotion regulation is a vital psychological process that allows people to manage their own emotional states. Recent psychological research has highlighted the importance of flexibility in emotion regulation, such that people can alternative between different emotion regulation strategies. A strategy is chosen depending upon the demands of the situation. This means that healthy emotion regulation is context-sensitive. This paper presents a computational model which models this form of flexible adaptation in emotion regulation in a simplified scenario in which the person has to switch between expressive suppression and attention modulation in managing anger in different work situations. Simulation results are reported that illustrate the capacity of the model to display adaptivity in emotion regulation across different contexts.

关键词： Emotion regulation Strategies Context Flexibility Adaptivity Psychopathology

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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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Pandemic drugs at pandemic speed: Infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- And physics-based simulations on high performance computers

arXiv

引用

arXiv 2021年

作者： Bhati, Agastya P. Wan, Shunzhou Alfè, Dario Clyde, Austin R. Bode, Mathis Tan, Li Titov, Mikhail Merzky, Andre Turilli, Matteo Jha, Shantenu Highfield, Roger R. Rocchia, Walter Scafuri, Nicola Succi, Sauro Kranzlmüller, Dieter Mathias, Gerald Wifling, David Donon, Yann Meglio, Alberto Di Vallecorsa, Sofia Ma, Heng Trifan, Anda Ramanathan, Arvind Brettin, Tom Partin, Alexander Xia, Fangfang Duan, Xiaotan Stevens, Rick Coveney, Peter V. Centre for Computational Science University College London Gordon Street LondonWC1H 0AJ United Kingdom Department of Earth Sciences London Centre for Nanotechnology and Thomas Young Centre University College London University College London Gower Street LondonWC1E 6BT United Kingdom Dipartimento di Fisica Ettore Pancini Università di Napoli Federico II Monte Sant’Angelo NapoliI-80126 Italy Department of Computer Science University of Chicago Institute for Combustion Technology RWTH Aachen University Aachen52056 Germany Brookhaven National Laboratory UptonNY11973 United States Rutgers State University of New Jersey PiscatawayNJ08854 United States Science Museum Exhibition Road LondonSW7 2DD United Kingdom Concept Lab Italian Institute of Technology Via Melen Genova Italy Center for Life Nanosciences at La Sapienza Italian Institute of Technology viale Regina Elena Roma Italy of the Bavarian Academy of Sciences and Humanities Boltzmannstraße 1 Garching bei München85748 Germany OpenLab CERN Geneva Switzerland Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Computing Environment and Life Sciences Directorate Argonne National Laboratory LemontIL60439 United States Institute for Informatics Science Park 904 University of Amsterdam Amsterdam1098 XH Netherlands

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers. © 2021, CC BY.

关键词： Molecular dynamics

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