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How generative Artificial Intelligence can transform drug discovery?

European journal of medicinal chemistry 2025年 295卷 117825页

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. Electronic address: tgaininsofia98@***. Faculty of Data Science and Computing Universiti Malaysia Kelantan Kota Bharu 16100 Kelantan Malaysia. Electronic address: akmal@umk.edu.my. 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. Electronic address: saberi@uaeu.ac.ae. LAMSADE Université Paris Dauphine - PSL Paris France. Electronic address: cazenave@lamsade.dauphine.fr. UKM Medical Molecular Biology Institute (UMBI) 56000 Kuala Lumpur Malaysia. Electronic address: chinsiokfong@ppukm.ukm.edu.my.

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.

关键词： database Drug discovery Drug-target interactions Generative artificial Intelligence Molecules representations Performance metrics Protein-protein interactions

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CEPV: A Tree Structure Information Extraction and Visualization Tool for big Knowledge Graph

CEPV: A Tree Structure Information Extraction and Visualizat...

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IEEE International Conference on big Knowledge (ICBK)

作者： Shaojing Sheng Peng Zhou Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Heifei Unversity of Technology Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China School of Computer Science and Technology Anhui University Heifei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Konowledge Science Heifei University of Technology Heifei China

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.

关键词： data visualization data mining Tools databases data processing Fault tolerance Fault tolerant systems

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Correction to: An empirical study on the joint impact of feature selection and data resampling on imbalance classification

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Applied Intelligence 2022年第7期53卷 8506-8506页

作者： Zhang, Chongsheng Soda, Paolo Bi, Jingjun Fan, Gaojuan Almpanidis, George García, Salvador Ding, Weiping Henan Key Lab of Big Data Analysis and Processing Henan University Henan China Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umea University Umea Sweden DaSCI Andalusian Research Institute University of Granada Granada Spain School of Information Science and Technology Nantong University Nantong China

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Geo-spatial ontology matching through compact evolutionary algorithm 2nd

Geo-spatial ontology matching through compact evolutionary a...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Xue, Xingsi Liu, Jianhua College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China

ISBN: (纸本)9783030045845

Geo-spatial ontologies can provide a formal description of concepts, relationships, activities, features and rules in GIS domain. However, simply use them only allows to partially solve semantic conflicts, and does not completely solve heterogeneity issues that are caused by themselves. Geo-spatial ontology matching technique can find the correspondences between semantic identical entities, and solve the heterogeneous problem between two geo-spatial ontologies. Be inspired by the successful application of Evolutionary Algorithm (EA) in instance matching domain, in this paper, it is utilized to match the heterogeneous geo-spatial ontologies. To reduce the runtime and memory consumption required by EA, a compact version of it is presented, which does not work on the whole population but a probability representation on it. In addition, a geo-spatial similarity measure is presented to determine the identical geo-spatial entities, and an optimal model is constructed for geo-spatial ontology matching problem. The experimental results show that cEA-based geo-spatial ontology matching technique can efficiently determine the alignment. © Springer Nature Switzerland AG 2019.

关键词： Evolutionary algorithms

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Gut microbiota and diabetes: From correlation to causality and mechanism

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World Journal of Diabetes 2020年第7期11卷 293-308页

作者： Wei-Zheng Li Kyle Stirling Jun-Jie Yang Lei Zhang Microbiome-X Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine and EngineeringKey Laboratory of Big Data-Based Precision MedicineBeihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Luddy School of Informatics Computing and EngineeringIndiana UniversityBloomingtonIN 47408United States The Crisis Technologies Innovation Lab Indiana UniversityThe Information Technology Services and the Pervasive Technology InstituteBloomingtonIN 47408United States Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Jinan 250000Shandong ProvinceChina College of Life Science Qilu Normal UniversityJinan 250000Shandong ProvinceChina Microbiome Research Center Shandong Institutes for Food and Drug ControlJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Qilu Children's Hospital of Shandong UniversityJinan 250000Shandong ProvinceChina Microbiological Laboratory Lin Yi People’s HospitalLinyi 276000Shandong ProvinceChina Qingdao Human Microbiome Center The Affiliated Central Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Institute for Medical Dataology Cheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Department of Biostatistics School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Research Institute of PediatricsQilu Children's HospitalCheeloo College of MedicineShandong Universityand Jinan Children's HospitalJinan 250022Shandong ProvinceChina

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for *** a global scale,diabetes causes a significant negative impact to the health status of human *** review covers type 1 diabetes and type 2 *** examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes *** appears that the human oral and gut microbiota are deeply interdigitated with *** is that *** studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and *** studies focus on the role and the potential impact of intestinal microflora in *** paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in *** microflora seems to have an endless capacity to impact and transform *** conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

关键词： Diabetes Microbiota Causality Mechanism Type 1 diabetes Type 2 diabetes Insulin resistance Inflammation Metabolites

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Phase diagrams of interacting spreading dynamics in complex networks

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Physical Review Research 2020年第2期2卷 023233-023233页

作者： Liming Pan Dan Yang Wei Wang Shimin Cai Tao Zhou Ying-Cheng Lai School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu 210023 China Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Cybersecurity Research Institute Sichuan University Chengdu 610065 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 610054 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA

Epidemic spreading processes in the real world can interact with each other in a cooperative, competitive, or asymmetric way, requiring a description based on coevolution dynamics. Rich phenomena such as discontinuous outbreak transitions and hystereses can arise, but a full picture of these behaviors in the parameter space is lacking. We develop a theory for interacting spreading dynamics on complex networks through spectral dimension reduction. In particular, we derive from the microscopic quenched mean-field equations a two-dimensional system in terms of the macroscopic variables, which enables a full phase diagram to be determined analytically. The diagram predicts critical phenomena that were known previously but only numerically, such as the interplay between discontinuous transition and hysteresis as well as the emergence and role of tricritical points.

关键词： Complex systems Epidemic Epidemic prevalence Network structure Epidemic spreading Spreading models

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BanFEL: A Blockchain Based Smart Contract for Fair and Efficient Lottery Scheme

BanFEL: A Blockchain Based Smart Contract for Fair and Effic...

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IEEE Conference on Dependable and Secure Computing

作者： Jiasheng Li Zijian Zhang Meng Li Beijing Engineering Research Center of Massive Language Information Beijing Institute of Technology Beijing China School of Computer Science University of Auckland Aukland New Zealand Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei University of Technology Anhui China

Lottery is a game with many people's dreams. But corruptions of lottery centers make the lottery unfair. To address this unfair issue, fair lottery schemes have been studied for several years. In these schemes, delay functions or aggregation protocols can be used to generate the winning numbers fairly. However, to the best of our knowledge, none of the existing schemes can achieve the winning number generated randomly, while the randomness can be verified fairly. In this article, we first propose a [B]lockchain based sm[a]rt co[n]tract for [F]air and [E]fficient [L]ottery (BanFEL) scheme. We further present a winning number random generation smart contract, by which players submit the purchased numbers and the non-tampering property is protected. Security analysis and experiments show that the fairness of BanFEL is protected better than the existing works. Meanwhile, the verification cost of this scheme are at least 0.03s lower than the traditional Grumbach's scheme between 1000 and 10000 players.

关键词： Blockchain Smart contracts Security Mathematical model Privacy Information processing

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Syntax-enhanced Pre-trained model

arXiv

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arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

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Spatio-temporal fusion for Macro- and Micro-expression Spotting in Long Video Sequences

Spatio-temporal fusion for Macro- and Micro-expression Spott...

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International Conference on Automatic Face and Gesture Recognition

作者： Li-Wei Zhang Jingting Li Su-Jing Wang Xian-Hua Duan Wen-Jing Yan Hai-Yong Xie Shu-Cheng Huang Key Laboratory of Behavior Sciences Institute of Psychology Chinese Academy of Sciences Beijing China School of Computer Jiangsu University of Science and Technology Zhenjiang Jiangsu China University of the Chinese Academy of Sciences Beijing China Institute of Psychology and Behavior Sciences Wenzhou University Wenzhou Zhejiang China School of Cyber Science University of Science and Technology of China HeFei Anhui China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC) Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

In this paper, we aim to construct a spotting framework automatically. It is still a great challenge to spot micro-expression(ME) intervals accurately due to short duration, low intensity, and shaking. Under the uncontrolled condition, the transformation is caused by head shaking. In order to remove the global movement caused by head shaking, we propose a simple yet effective method to disentangle local movement vector from the global optical flow field by the estimation of mean optical flow in the nose region. After preprocessing, we extract the completed specific pattern(SP) of ME in each region of interest(ROI). The pattern consists of two sub-patterns: magnitude and angle. However, influenced by frame rate and different intensities of micro- and macro- expressions, we propose to use a multi-scale filter to improve the ability to spot both micro- and macro-expressions. The spotting result performed on CAS(ME) 2 and SAMM shows that our proposed method outperforms the basline method.

关键词： Optical imaging Feature extraction Nose Optical filters Faces databases Face recognition

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Analysis of bivariate time series of magnetoencephalography for depression Based on Granger Kernel Method 3

Analysis of bivariate time series of magnetoencephalography ...

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3rd International Conference on Biological Information and Biomedical engineering, BIBE 2019

作者： Hu, Jialin Yan, Wei Wang, Jun Li, Jin Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Psychiatry Affiliated Brain Hospital of Nanjing Medical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China School of Science China Pharmaceutical University Nanjing China

ISBN: (纸本)9783800750269

Granger causality is widely used to analyze dynamic systems, among which the linear granger causality method is a very popular method to analyze the causality between bivariate time series. In this paper, an improved granger causality method, kernel granger causality method, is adopted to analyze the causality of the time series of symmetrical channels in the left and right frontal areas of magnetoencephalography (MEG) in normal people and depressed patients by specifically using the inhomogeneous polynomial(IP) kernel method and Gaussian kernel. The results showed that both methods showed that the causality index from the right frontal area to the left frontal area is greater than in the opposite direction in both healthy subjects and depressed patients. Different from Gaussian kernel method, the results of Inhomogeneous polynomial kernel also showed that the causality index of depressed patients was more susceptible to stimulation. Specifically, the causality index of both directions for depressed patients was higher than that of normal people under negative and neutral stimulation. By comparing the two different kernel methods, we found that the inhomogeneous polynomial kernel is a more appropriate method to distinguish depressed patients from normal people than the Gaussian kernel in our study. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Magnetoencephalography

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