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How generative Artificial Intelligence can transform drug discovery?

European Journal of Medicinal Chemistry 2025年 295卷

作者： Ainin Sofia Jusoh Muhammad Akmal Remli Mohd Saberi Mohamad Tristan Cazenave Chin Siok Fong Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kota Bharu 16100 Kelantan Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan Kota Bharu 16100 Kelantan Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University Al Ain 15551 United Arab Emirates Faculty of Engineering and Technology Multimedia University 75450 Melaka Malaysia Department of Biosystems Engineering Faculty of Agricultural Technology Universitas Brawijaya 65145 Malang East Java Indonesia LAMSADE Université Paris Dauphine - PSL Paris France UKM Medical Molecular Biology Institute (UMBI) 56000 Kuala Lumpur Malaysia

Generative Artificial Intelligence (Generative AI) is transforming drug discovery by enabling advanced analysis of complex biological and chemical data. This review explores key Generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models and Transformer-based models, with Transformers gaining prominence due to the abundance of text-based biological data and the success of language models like ChatGPT. The paper discusses molecular representations, performance evaluation metrics, and current trends in Generative AI-driven drug discovery, such as protein-protein interactions (PPIs), drug-target interactions (DTIs) and de-novo drug design. However, these approaches face significant challenges, including applicability domain issues, lack of interpretability, data scarcity, novelty, scalability, computational resource limitations, and the absence of standardized evaluation metrics. These challenges hinder model performance, complicate decision-making, and limit the generation of novel and viable drug candidates. To address these issues, strategies such as hybrid models, integration of multiomics datasets, explainable AI (XAI) techniques, data augmentation, transfer learning, and cloud-based solutions are proposed. Additionally, a curated list of databases supporting drug discovery research is provided. The review concludes by emphasizing the need for optimized AI models, robust validation methods, interdisciplinary collaboration, and future academic efforts to fully realize the potential of Generative AI in advancing drug discovery.

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Predicting news values from headline text and emotions 2

Predicting news values from headline text and emotions

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EMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017

作者： Di Buono, Maria Pia Šnajder, Jan Bašic, Bojana Dalbelo Glavaš, Goran Tutek, Martin Milic-Frayling, Natasa TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Croatia Data and Web Science Group University of Mannheim Germany School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9781945626883

We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models - an SVM and a CNN - to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information. © EMNLP *** right reserved.

关键词： Forecasting

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A Behavioural Network Traffic Novelty Detection for the Internet of Things Infrastructures 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Hamad, Salma Abdalla Sheng, Quan Z. Tran, Dai Hoang Zhang, Wei Emma Nepal, Surya Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia School of Computer Science The University of Adelaide Adelaide Australia Data61 CSIRO Sydney Australia

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

The Internet of Things (IoT) applied solutions are changing the way the world perceives technology. IoT devices are now being used in a wide range of applications to transfer or share relevant information, hence reducing human interventions. With such widespread IoT solutions, security becomes a significant concern. Many of the IoT devices are vulnerable due to several reasons, including in-secure implementations, poor life cycle management, and inappropriate configurations, leading to an increase in the risk of these devices getting exposed and attacked. However, the current security approaches for detecting compromised IoT devices are inefficient, especially for zero-day attacks. Since no one knows how a new attack would look like, it will be useful to monitor and detect anomalies using accurate detection techniques. This work probes the possibility of detecting IoT network traffic anomalies using novelty detection techniques;thus, it can detect compromised IoT devices. One of this work’s main contributions is developing an IoT anomaly detection system named Behavioural Novelty Detection for IoT Infrastructure (BND-IoT). BND-IoT trains a neural network with novel selected behavioural features extracted from benign traffic only and then uses the novelty techniques to detect any unusual traffic patterns. We show that the presented approach effectively detects anomalies within IoT devices’ network traffic with a robust average F1-score of 96.7% and a low false rejection rate of 7%. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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2828 The influence of ethnicity and social disadvantage on frailty in a United Kingdom (UK) populationGet access

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Age and Ageing 2025年第SUPPLEMENT_1期54卷

作者： Heald, AH Lu, W Williams, R McCay, K Stedman, M O’Neill, TW The School of Medicine and Manchester Academic Health Sciences Centre University of Manchester Department of Endocrinology and Diabetes Salford Royal Hospital Salford Department of Computing & Mathematics Faculty of Science and Engineering Manchester Metropolitan University Division of Informatics Imaging and Data Science University of Manchester RES Consortium Andover Centre for Epidemiology Versus Arthritis University of Manchester Department of Rheumatology Salford UK

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A Machine Learning Approach to Predict the Trend of Obesity Prevalence at a Global Level 6

A Machine Learning Approach to Predict the Trend of Obesity ...

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6th IEEE/ACIS International Conference on Big data, Cloud computing, and data science, BCD 2021

作者： Barzinji, Ala Othman Ma, Chaoying Du, Wencai Ma, Jixin School of Computing and Mathematical Sciences Faculty of Liberal Arts and Sciences University of Greenwich United Kingdom Institute of Data Science City University of Macau China

ISBN: (纸本)9781728176819

Excessive weight is associated with adverse health risks. Understanding the global trends in Obesity, in children and adolescents, is paramount to impede the increasing rate of global Obesity prevalence. Different machine learning models were used to predict Obesity prevalence, in children and adolescents, at a global level. This paper presents a novel approach to predict Obesity beyond 2030 using machine learning. The data was derived from a global population-based survey in 2015. In the main study, we applied machine learning models to predict the exponential rise in Obesity prevalence across the world in 2030, 2040, and 2050. In the second study, we further calculated the Obesity prevalence rates according to Socio-Demographic Index (SDI). We obtained promising results with model prediction accuracies of up to 99% R2 for the main study, and up to 92% R2 for the SDI study. © 2021 IEEE.

关键词： Health risks

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Classification-based self-learning for weakly supervised bilingual lexicon induction 58

Classification-based self-learning for weakly supervised bil...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Karan, Mladen Vulić, Ivan Korhonen, Anna Glavaš, Goran TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Language Technology Lab TAL University of Cambridge Data and Web Science Group University of Mannheim

ISBN: (纸本)9781952148255

Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present CLASSYMAP, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of CLASSYMAP for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs. © 2020 Association for Computational Linguistics

关键词： Iterative methods

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Female sex hormones and the oral contraceptive pill modulate asthma severity through GLUT-1

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Mucosal Immunology 2025年

作者： Brown, Alexandra C. Carroll, Olivia R. Mayall, Jemma R. Zounemat-Kermani, Nazanin Vinzenz, Samantha L.E. Gomez, Henry M. Mills, Ed F. Kim, Richard Y. Donovan, Chantal Baines, Katherine J. Williams, Evan J. Berthon, Bronwyn S. Wynne, Katie Scott, Hayley A. Pinkerton, James W. Guo, Yike Hansbro, Philip M. Foster, Paul S. Wark, Peter A.B. Dahlen, Sven-Erik Adcock, Ian M. Wood, Lisa G. Horvat, Jay C. The University of Newcastle and Immune Health Program Hunter Medical Research Institute Newcastle NSW Australia The Data Science Institute Department of Computing Imperial College London United Kingdom School of Life Sciences Faculty of Science University of Technology Sydney Sydney NSW Australia Respiratory Pharmacology & Toxicology Group National Heart & Lung Institute Imperial College London London United Kingdom Centre for Inflammation Centenary Institute and Faculty of Science University of Technology Sydney Sydney NSW Australia Clinical Lung and Allergy Research Unit Department of Medicine Huddinge Karolinska Institutet and Department of Respiratory Medicine and Allergy Karolinska University Hospital Huddinge and Unit of Integrative Metabolomics Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden The Airway Disease Section National Heart & Lung Institute Imperial College London London United Kingdom Woolcock Institute of Medical Research Sydney NSW Australia School of Translational Medicine Monash University Melbourne VIC Australia Woolcock Institute of Medical Research and Faculty of Medicine Health and Human Sciences Macquarie University Sydney NSW Australia Hong Kong University of Science and Technology Hong Kong

Females are disproportionately affected by asthma. An increased understanding of how female sex hormones influence key pathophysiological processes that underpin asthma may identify new, more effective asthma therapies, particularly for females with severe, poorly controlled asthma. We assessed the effects of oral ethinylestradiol/levonorgestrel (representing OCP use) and depot-medroxyprogesterone acetate (DMPA) and estradiol injections on key features of experimental asthma, and determined their effects on glucose transporter-1 (GLUT-1). The effects of OCP use on clinical asthma outcomes, and the relationships between estrogen receptors and type 2 (T2), non-T2, and GLUT-1 responses, in clinical asthma were also determined. OCP and DMPA reduce T2 responses, disease features, and lung expression of GLUT-1, whereas estradiol increases lung expression of GLUT-1, and results in severe, corticosteroid-insensitive, neutrophil-enriched disease, in experimental asthma. OCP use is associated with reduced T2 cytokine and GLUT-1 responses in clinical asthma. GLUT-1 expression is increased in sputum of severe asthmatics, and positively correlates with estrogen receptor expression and both T2 and non-T2 inflammatory responses. Significantly, OCP or GLUT-1 inhibition protects against obesity-associated or estradiol-induced, severe, experimental asthma, respectively. Together, these data show how female sex hormones and the OCP likely modulate asthma severity by modifying GLUT-1 responses in the airways. © 2025 The Authors

关键词： Asthma Estrogen GLUT-1 oral contraceptive pill (OCP) Progesterone

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Improving the Cost of Updates in Virtual Knowledge Graphs

Improving the Cost of Updates in Virtual Knowledge Graphs

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Wandji, Romuald Esdras Calvanese, Diego Department of Computing Science Umeå Universitet Umeå Sweden Research Centre for Knowledge and Data Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy

Virtual Knowledge Graph (VKG) is known as a data integration paradigm used to efficiently manage the heterogeneity of richly structured data that is common inside several organizations, in inter-organizational settings, and more openly on the Web. Although such a paradigm continues to gain importance in both foundational and applied research, updates in VKG systems remain an open challenge that has received less attention. Yet, a solution to such a problem would be of great importance, as it would allow VKG systems to be full-fledged, thus allowing end-users to fully manage source data through the lens of the ontology they are exposed to. This research aims to propose a comprehensive framework for instance-level updates in VKGs, where updates posed over the ontology have to be translated into source-level updates and, more importantly, how the side effects related to the propagation of ontology-based updates to the underlying data source can be minimized. © 2024 Copyright for this paper by its authors.

关键词： Knowledge graph

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Towards Techniques for Updating Virtual Knowledge Graphs 17

Towards Techniques for Updating Virtual Knowledge Graphs

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17th International Rule Challenge and 7th Doctoral Consortium @ RuleM+RR, RuleML+RR-Companion 2023

作者： Wandji, Romuald Esdras Šimkus, Mantas Calvanese, Diego Department of Computing Science Umeå Universitet Umeå Sweden Research Centre for Knowledge and Data Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy

The field of Virtual Knowledge Graphs (VKGs) continues to grow in both academic and applied contexts. Yet, the issue of updates in VKG systems has not yet received adequate attention, although it is crucial to manage data modifications at the data source level through the lens of an ontology. In this paper, we focus on VKGs whose ontology is specified in the lightweight ontology language DL-LiteA, and we propose diverse settings and research directions we intend to explore to address the challenge of translating ontology-based updates into updates at the level of data sources. We also pay attention to the important problem of automated analysis of mappings, which plays a major role when it comes to reformulating ontology-based update requests into update requests over the data sources. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

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Semantic Querying of Integrated Raster and Relational data: A Virtual Knowledge Graph Approach 17

Semantic Querying of Integrated Raster and Relational Data: ...

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17th International Rule Challenge and 7th Doctoral Consortium @ RuleM+RR, RuleML+RR-Companion 2023

作者： Ghosh, Arka Šimkus, Mantas Calvanese, Diego Department of Computing Science Umeå Universitet Umeå Sweden Research Centre for Knowledge and Data Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy

Ontology-based data access (OBDA) facilitates access to heterogeneous data sources through the mediation of an ontology (e.g. OWL), which captures the domain of interest and is connected to data sources through a declarative mapping. In our study, large, heterogeneous earth observational (EO) data, known as raster data, and geometrical data, known as vector data, are considered as (heterogeneous) data sources. Raster data represent, e.g., Earth's natural phenomena, such as surface temperature, elevation, or air pollution, as multidimensional arrays. In contrast, vector data depict, e.g., locations, networks, or regions on Earth, using geometries. Domain experts, such as earth scientists and GIS practitioners, still struggle to undertake advanced studies by querying large raster and vector data in an integrated way because, unlike relational data, they come in diverse formats and different data structures. In our approach to integration, we use a geospatial extension of an RDBMS to represent vector data as relational data, and a domain-agnostic array DBMS to handle raster data. Our aim is to extend the OBDA paradigm to effectively deal with relational, vector, and raster data in a combined way, while leveraging the built-in capabilities of data management tools relevant to each type of data. We also plan to develop techniques to calculate on the fly for each user query posed over the ontology an optimal query plan that exploits, at best, the query processing capabilities of each tool, while limiting costly data transfer operations between tools. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Semantics

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