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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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Semi-unsupervised lifelong learning for sentiment classification: Less manual data annotation and more self-studying

arXiv

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arXiv 2019年

作者： Hong, Xianbin Wong, Prudence Pal, Gautam Liu, Dawei Huang, Xin Guan, Sheng-Uei Man, Ka Lok Research Institute of Big Data Analytics Xi'an Jiaotong-Liverpool University Suzhou Jiangsu China Department of Computer Science University of Liverpool Liverpool Merseyside United Kingdom

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Naïve Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focus on how to accumulate knowledge during learning and leverage them for the further tasks. Meanwhile, the demand for labeled data for training also be significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labeled data and computational cost to achieve the performance as well as or even better than the supervised learning. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Synthesis and cytotoxicity of indolin-2-one derivatives against WiDr cells

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AIP Conference Proceedings 2021年第1期2349卷

作者： Arif Fadlan Budi Prayitno Muhammad R. G. Fahmi Mardi Santoso Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 East Java Indonesia

The synthesis of indolin-2-one derivatives and in vitro anticancer activity on colon cancer were reported in the present study. The compounds contain a fluorine or a bromine in position 5, and a bromine in position 7. The results established that indolin-2-one with bromine in position 5 gave up to a 4-fold improved cytotoxicity over indolin-2-one with fluorine in position 5. The indolein-2-one with bromine in posititon 5 also exerted higher cytotoxicity compared to bromine in position 7. This work highlights the importance of halogen and their position in cytotoxicity.

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Consensus dynamics in online collaboration systems

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Computational Social Networks 2018年第1期5卷 2页

作者： Hasani-Mavriqi, Ilire Kowald, Dominik Helic, Denis Lex, Elisabeth Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics Inffeldgasse 13/6 Graz 8010 Austria Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 13/6 Graz 8010 Austria

Background: In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics. Methods: For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web. Results: Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process. Conclusions: In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors. © 2018, The Author(s).

关键词： Consensus dynamics Interaction networks Online collaboration systems Similarity Social status

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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Sharpening semantic gradient in a planarized sentence representation

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Neural Networks 2025年 190卷

作者： Caiwei Yang Yanping Chen Shuai Yu Bo Dong Jiwei Qin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang 550025 Guizhou PR China State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China Department of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 Shanxi PR China College of Information Science and Engineering Xinjiang University Urumqi 830046 Xinjiang PR China

Mapping a sentence into a two-dimensional representation has the advantage of unfolding nested semantic structures in a sentence and encoding the interaction between tokens. In the planarized sentence representation, neighboring elements denote overlapped linguistic units in a sentence. An important phenomenon is that the semantic information of a true linguistic unit may penetrate neighboring elements, which blurs the semantic edge of a linguistic unit and disturbs the planarized sentence representation. Therefore, sharpening the semantic gradient helps aggravate semantic information from neighborhoods and depressing noises in neighboring elements. This paper reveals the mechanism of sharpening semantic gradient in the planarized sentence representation. Our method is evaluated on six evaluation datasets. The results show impressive improvement on three information extraction tasks. The success indicates that representing and processing sentences in a two-dimensional representation has a great potential to decode the sentential semantic structure and support sentence-level information extraction. Our code to implement the model is available at: https://***/caiwyang/Semantic_Gradient .

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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SSM-DTA: Breaking the Barriers of data Scarcity in Drug-Target Affinity Prediction

arXiv

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arXiv 2022年

作者： Pei, Qizhi Wu, Lijun Zhu, Jinhua Xia, Yingce Xie, Shufang Qin, Tao Liu, Haiguang Liu, Tie-Yan Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China No.59 Zhong Guan Cun Avenue Haidian District Beijing100872 China Microsoft Research AI4Science No.5 Dan Ling Street Haidian District Beijing100080 China CAS Key Laboratory of GIPAS EEIS Department University of Science and Technology of China No.96 JinZhai Road Baohe District Anhui Province Hefei230026 China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the SSM-DTA framework, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling (MLM) using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations, and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes. Our code is available at https://***/QizhiPei/SSM-DTA. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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METHODS of EXTRACTING ELECTROCARDIOGRAMS from ELECTRONIC SIGNALS and IMAGES in the PYTHON ENVIRONMENT

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Diagnostyka 2020年第3期21卷 95-101页

作者： Zholmagambetova, Bakhytgul Mazakov, Talgat Jomartova, Sholpan Izat, Adilzhan Bibalayev, Olzhas Department of Information Technology and Security Karaganda State Technical University Shakhtarov Avenue 70-191 Karaganda100000 Kazakhstan Department of Computer Science Al-Farabi Kazakh National University Zhanar st. 37a1 Almaty500045 Kazakhstan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Zhanar st. 37a1 Almaty500045 Kazakhstan Department of Applied Mathematics and Computer Science Karaganda State University named after Academician E. A. Buketov Shakhtarov Avenue 70-191 Karaganda100000 Kazakhstan Department of Robotics and Intelligent Machine Scientific Research Laboratory Karaganda State University named after Academician E. A. Buketov Satybaldina str. 16-53 Karaganda100000 Kazakhstan

High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies. © 2020 Polish Society of Technical Diagnostics. All rights reserved.

关键词： Electrocardiograms

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Variation in Sedative and Analgesic Use During the COVID-19 Pandemic and Associated Outcomes

CHEST Critical Care

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CHEST Critical Care 2024年第1期2卷 100047-100047页

作者： Rucci, Justin M. Law, Anica C. Bolesta, Scott Quinn, Emily K. Garcia, Michael A. Gajic, Ognjen Boman, Karen Yus, Santiago Goodspeed, Valerie M. Kumar, Vishakha Kashyap, Rahul Walkey, Allan J. Mesland, Jean-Baptiste Henin, Pierre Petre, Hélène Buelens, Isabelle Gerard, Anne-Catherine Markotić, Dragana Bošnjak, Ivana Garza, Ruben Chu, Eric Chan, Victoria Gavidia, Oscar Y. Pachon, Felipe El Kassas, Mohamed Tawheed, Ahmed Matsuda, Wataru Itagaki, Yuki Kodate, Akira Suzuki, Reina Takahashi, Yuki Moriki, Koyo Amer, Marwa Ridha Bawazeer, Mohammed Abdullah Dahhan, Talal I. Kseibi, Eiad Butt, Abid Shahzad Khurshid, Syed Moazzum Rabee, Muath Abujazar, Mohammed Alghunaim, Razan Abualkhair, Maal AlFirm, Abeer Turki Almazyad, Mohammed A. Alarifi, Mohammed I. Macarambon, Jara M. Bukhari, Ahmad Abdullah Albahrani, Hussain A. Asfina, Kazi N. Aldossary, Kaltham M. Bojicic, Jovana Kovacevic, Bojan Predrag, Stevanovic Stojakov, Dejan S. Ignjatovic, Duska K. Bojic, Suzana C. Bobos, Marina M. Nenadic, Irina B. Zaric, Milica S. Djuric, Marko D. Djukic, Vladimir R. Teruel, Santiago Y. Martin, Belen C. Kharbanda, Anmol Jhajhria, Sunil Fyffe, Zachary Capizzi, Stephen Alicie, Bethany Green, Martha Crockarell, Lori Drennan, Amelia Dubuque, Kathleen Fambrough, Tonya Gasaway, Nikole Krantz, Briana Nebi, Peiman Orga, Jan Serfass, Margaret Simion, Alina Warren, Kimberly Wheeler, Cassie Woolman, C.J. Christie, Amy B. Ashley, Dennis W. Adiga, Rajani Newsome, Andrea Sikora Forehand, Christy C. Bruning, Rebecca Jones, Timothy W. Waikar, Sushrut S. Colona, Mia Kibbelaar, Zoe Leong, Michael Wallman, Daniel Soni, Kanupriya Maccarone, Jennifer Gilman, Joshua Devis, Ycar Chung, Joseph Paracha, Munizay Lumelsky, David N. DiLorenzo, Madeline Abdurrahman, Najla Johnson, Shelsey Tirupathi, Raghavendra Tang, Alymer Safi, Arshad Green, Cindy Newell, Jackie Ramani, Naga Ganti, Bhavani Harika Daugherty, Steven K. Atkinson, Sam Shrimpton, Kelly Smalls, Norma Reilkoff, Ronald A. Heneghan, Julia A. Eichen, Sarah Goertzen, Lexie Rajala, Scott Feussom, Ghislaine Ta The Pulmonary Center The Department of Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA Center for Healhcare Organization and Implementation Research VA Boston Healthcare System Boston MA Department of Pharmacy Practice Nesbitt School of Pharmacy Wilkes University Wilkes-Barre PA United States Biostatistics and Epidemiology Data Analytics Center Boston University School of Public Health Boston MA Division of Pulmonary and Critical Care Medicine Department of Medicine University of Washington Medicine Valley Medical Center Renton WA Division of Pulmonary and Critical Care Medicine Department of Medicine Mayo Clinic Rochester MN Society of Critical Care Medicine Mount Prospect IL Department of Intensive Care Medicine La Paz University Hospital Madrid Spain Department of Anesthesia Critical Care and Pain Medicine Beth Israel Deaconess Medical Center Boston MA Department of Anesthesia and Perioperative Medicine Mayo Clinic Rochester MN Division of Health Systems Science Department of Medicine University of Massachusetts Chan School of Medicine Worcester MA

Background: Providing analgesia and sedation is an essential component of caring for many mechanically ventilated patients. The selection of analgesic and sedative medications during the COVID-19 pandemic, and the impact of these sedation practices on patient outcomes, remain incompletely characterized. Research Question: What were the hospital patterns of analgesic and sedative use for patients with COVID-19 who received mechanical ventilation (MV), and what differences in clinical patient outcomes were observed across prevailing sedation practices? Study Design and Methods: We conducted an observational cohort study of hospitalized adults who received MV for COVID-19 from February 2020 through April 2021 within the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) COVID-19 Registry. To describe common sedation practices, we used hierarchical clustering to group hospitals based on the percentage of patients who received various analgesic and sedative medications. We then used multivariable regression models to evaluate the association between hospital analgesia and sedation cluster and duration of MV (with a placement of death [POD] approach to account for competing risks). Results: We identified 1,313 adults across 35 hospitals admitted with COVID-19 who received MV. Two clusters of analgesia and sedation practices were identified. Cluster 1 hospitals generally administered opioids and propofol with occasional use of additional sedatives (eg, benzodiazepines, alpha-agonists, and ketamine);cluster 2 hospitals predominantly used opioids and benzodiazepines without other sedatives. As compared with patients in cluster 2, patients admitted to cluster 1 hospitals underwent a shorter adjusted median duration of MV with POD (β-estimate, –5.9;95% CI, –11.2 to –0.6;P =.03). Interpretation: Patients who received MV for COVID-19 in hospitals that prioritized opioids and propofol for analgesia and sedation experienced

关键词： acute respiratory failure analgesia COVID-19 mechanical ventilation sedation

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