AI-DRIVEN DRUG DISCOVERY:
ACCELERATING THE DEVELOPMENT OF NOVEL THERAPEUTICS IN BIOPHARMACEUTICALS
DOI:
https://doi.org/10.60087/jklst.vol3.n3.p.206-224Keywords:
Artificial Intelligence, Drug Discovery, Biopharmaceuticals, Machine LearningAbstract
Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential.
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Akbar, A., Peoples, N., Xie, H., Sergot, P., Hussein, H., Peacock IV, W. F., & Rafique, Z. . (2022). Thrombolytic Administration for Acute Ischemic Stroke: What Processes can be Optimized?. McGill Journal of Medicine, 20(2).
Chen, P., Lam, K. H., Liu, Z., Mindlin, F. A., Chen, B., Gutierrez, C. B., ... & Jin, R. (2019). Structure of the full-length Clostridium difficile toxin B. Nature structural & molecular biology, 26(8), 712-719.
Zhang, X., Wang, C., Chen, B., Wang, Q., Xu, W., Ye, S., ... & Zhang, R. (2019). Crystal structure of refolding fusion core of Lassa virus GP2 and design of Lassa virus fusion inhibitors. Frontiers in microbiology, 10, 1829.
Chen, B., Basak, S., Chen, P., Zhang, C., Perry, K., Tian, S., ... & Jin, R. (2022). Structure and conformational dynamics of Clostridioides difficile toxin A. Life science alliance, 5(6).
Chen, B., Zhu, Y., Ye, S., & Zhang, R. (2018). Structure of the DNA-binding domain of human myelin-gene regulatory factor reveals its potential protein-DNA recognition mode. Journal of structural biology, 203(2), 170-178.
Chen, B., Liu, Z., Perry, K., & Jin, R. (2022). Structure of the glucosyltransferase domain of TcdA in complex with RhoA provides insights into substrate recognition. Scientific reports, 12(1), 9028.
Chen, Baohua, Kay Perry, and Rongsheng Jin. "Neutralizing epitopes on Clostridioides difficile toxin A revealed by the structures of two camelid VHH antibodies." Frontiers in Immunology 13 (2022): 978858.
Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.
Shi J, Shang F, Zhou S, et al. Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy[J]. Journal of Industrial Engineering and Applied Science, 2024, 2(4): 90-103.
Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 108-126.
Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.
Lei, H., Wang, B., Shui, Z., Yang, P., & Liang, P. (2024). Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology. arXiv preprint arXiv:2404.04492.
Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.
Xu, Y., Liu, Y., Xu, H., & Tan, H. (2024). AI-Driven UX/UI Design: Empirical Research and Applications in FinTech. International Journal of Innovative Research in Computer Science & Technology, 12(4), 99-109.
Liu, Y., Xu, Y., & Song, R. (2024). Transforming User Experience (UX) through Artificial Intelligence (AI) in interactive media design. Engineering Science & Technology Journal, 5(7), 2273-2283.
Zhang, P. (2024). A STUDY ON THE LOCATION SELECTION OF LOGISTICS DISTRIBUTION CENTERS BASED ON E-COMMERCE. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 103-107.
Zhang, P., & Gan, L. I. U. (2024). Optimization of Vehicle Scheduling for Joint Distribution in Logistics Park based on Priority. Journal of Industrial Engineering and Applied Science, 2(4), 116-121.
Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-world Data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69.
Ping, G., Wang, S. X., Zhao, F., Wang, Z., & Zhang, X. (2024). Blockchain Based Reverse Logistics Data Tracking: An Innovative Approach to Enhance E-Waste Recycling Efficiency.
Xu, H., Niu, K., Lu, T., & Li, S. (2024). Leveraging artificial intelligence for enhanced risk management in financial services: Current applications and future prospects. Engineering Science & Technology Journal, 5(8), 2402-2426.
Shi, Y., Shang, F., Xu, Z., & Zhou, S. (2024). Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores. Journal of Artificial Intelligence and Development, 3(1), 40-46.
Wang, Shikai, Kangming Xu, and Zhipeng Ling. "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 77-87.
Ping, G., Zhu, M., Ling, Z., & Niu, K. (2024). Research on Optimizing Logistics Transportation Routes Using AI Large Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 14-27.
Shang, F., Shi, J., Shi, Y., & Zhou, S. (2024). Enhancing E-Commerce Recommendation Systems with Deep Learning-based Sentiment Analysis of User Reviews. International Journal of Engineering and Management Research, 14(4), 19-34.
Xu, H., Li, S., Niu, K., & Ping, G. (2024). Utilizing Deep Learning to Detect Fraud in Financial Transactions and Tax Reporting. Journal of Economic Theory and Business Management, 1(4), 61-71.
Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. arXiv preprint arXiv:2403.19345.
Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility.
Zheng, H., Xu, K., Zhou, H., Wang, Y., & Su, G. (2024). Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis. Academic Journal of Science and Technology, 10(1), 62-68.
Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Applied and Computational Engineering 2024, 87, 26–32.
Zhan, X., Shi, C., Li, L., Xu, K., & Zheng, H. (2024). Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models. Applied and Computational Engineering, 71, 21-26.
Li, P., Hua, Y., Cao, Q., & Zhang, M. (2020, December). Improving the Restore Performance via Physical-Locality Middleware for Backup Systems. In Proceedings of the 21st International Middleware Conference (pp. 341-355).
Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). THE IMPACT OF PRICING SCHEMES ON CLOUD COMPUTING AND DISTRIBUTED SYSTEMS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 193-205.
Liu, B. (2023). Based on intelligent advertising recommendation and abnormal advertising monitoring system in the field of machine learning. International Journal of Computer Science and Information Technology, 1(1), 17-23.
Shang, F., Zhao, F., Zhang, M., Sun, J., & Shi, J. (2024). Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences. International Journal of Innovative Research in Engineering and Management, 11(4), 39-49.
Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Applied and Computational Engineering, 67, 1-7.
Wu, B., Gong, Y., Zheng, H., Zhang, Y., Huang, J., & Xu, J. (2024). Enterprise cloud resource optimization and management based on cloud operations. Applied and Computational Engineering, 67, 8-14.
Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J., & Wan, W. (2024). Application of machine learning optimization in cloud computing resource scheduling and management. Applied and Computational Engineering, 64, 9-14.
Liu, B., & Zhang, Y. (2023). Implementation of seamless assistance with Google Assistant leveraging cloud computing. Journal of Cloud Computing, 12(4), 1-15.
Guo, L., Li, Z., Qian, K., Ding, W., & Chen, Z. (2024). Bank Credit Risk Early Warning Model Based on Machine Learning Decision Trees. Journal of Economic Theory and Business Management, 1(3), 24-30.
Xu, Z., Guo, L., Zhou, S., Song, R., & Niu, K. (2024). Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 1-7.
Guo, L.; Song, R.; Wu, J.; Xu, Z.; Zhao, F. Integrating a Machine Learning-Driven Fraud Detection System Based on a Risk Management Framework. Preprints 2024, 2024061756.
Feng, Y., Qi, Y., Li, H., Wang, X., & Tian, J. (2024, July 11). Leveraging federated learning and edge computing for recommendation systems within cloud computing networks. In Proceedings of the Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024) (Vol. 13210, pp. 279-287). SPIE.
Gong, Y., Liu, H., Li, L., Tian, J., & Li, H. (2024, February 28). Deep learning-based medical image registration algorithm: Enhancing accuracy with dense connections and channel attention mechanisms. Journal of Theory and Practice of Engineering Science, 4(02), 1-7.
Yu, K., Bao, Q., Xu, H., Cao, G., & Xia, S. (2024). An Extreme Learning Machine Stock Price Prediction Algorithm Based on the Optimisation of the Crown Porcupine Optimisation Algorithm with an Adaptive Bandwidth Kernel Function Density Estimation Algorithm.
Li A, Zhuang S, Yang T, Lu W, Xu J. Optimization of logistics cargo tracking and transportation efficiency based on data science deep learning models. Applied and Computational Engineering. 2024 Jul 8;69:71-7.
Xu, J., Yang, T., Zhuang, S., Li, H. and Lu, W., 2024. AI-based financial transaction monitoring and fraud prevention with behaviour prediction. Applied and Computational Engineering, 77, pp.218-224.
Ling, Z., Xin, Q., Lin, Y., Su, G. and Shui, Z., 2024. Optimization of autonomous driving image detection based on RFAConv and triplet attention. Applied and Computational Engineering, 77, pp.210-217.
He, Z., Shen, X., Zhou, Y., & Wang, Y. (2024, January). Application of K-means clustering based on artificial intelligence in gene statistics of biological information engineering. In Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing (pp. 468-473).
Gong, Y., Zhu, M., Huo, S., Xiang, Y., & Yu, H. (2024, March). Utilizing Deep Learning for Enhancing Network Resilience in Finance. In 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) (pp. 987-991). IEEE.
Xin, Q., Xu, Z., Guo, L., Zhao, F., & Wu, B. (2024). IoT traffic classification and anomaly detection method based on deep autoencoders. Applied and Computational Engineering, 69, 64-70.
Yang, T., Li, A., Xu, J., Su, G. and Wang, J., 2024. Deep learning model-driven financial risk prediction and analysis. Applied and Computational Engineering, 77, pp.196-202.
Zhou, Y., Zhan, T., Wu, Y., Song, B., & Shi, C. (2024). RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models. arXiv preprint arXiv:2405.06655.
Liu, B., Cai, G., Ling, Z., Qian, J., & Zhang, Q. (2024). Precise Positioning and Prediction System for Autonomous Driving Based on Generative Artificial Intelligence. Applied and Computational Engineering, 64, 42-49.
Ping G, Wang S X, Zhao F, et al. Blockchain Based Reverse Logistics Data Tracking: An Innovative Approach to Enhance E-Waste Recycling Efficiency[J]. 2024.
Lu, W., Ni, C., Wang, H., Wu, J., & Zhang, C. (2024). Machine Learning-Based Automatic Fault Diagnosis Method for Operating Systems.
Song, R., Wang, Z., Guo, L., Zhao, F., & Xu, Z. (2024). Deep Belief Networks (DBN) for Financial Time Series Analysis and Market Trends Prediction.World Journal of Innovative Medical Technologies, 5(3), 27–34.
Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Preprints 2024, 2024070595.
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