Version 1
: Received: 13 October 2024 / Approved: 14 October 2024 / Online: 14 October 2024 (14:29:13 CEST)
How to cite:
Huang, D.; Yang, M.; Zheng, W. Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification. Preprints2024, 2024101089. https://doi.org/10.20944/preprints202410.1089.v1
Huang, D.; Yang, M.; Zheng, W. Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification. Preprints 2024, 2024101089. https://doi.org/10.20944/preprints202410.1089.v1
Huang, D.; Yang, M.; Zheng, W. Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification. Preprints2024, 2024101089. https://doi.org/10.20944/preprints202410.1089.v1
APA Style
Huang, D., Yang, M., & Zheng, W. (2024). Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification. Preprints. https://doi.org/10.20944/preprints202410.1089.v1
Chicago/Turabian Style
Huang, D., Mingxuan Yang and Wenxuan Zheng. 2024 "Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification" Preprints. https://doi.org/10.20944/preprints202410.1089.v1
Abstract
Artificial intelligence (AI) shows great potential in medical diagnosis and drug analysis. Through deep learning technology, AI can automatically extract features from large amounts of complex medical data, significantly improving diagnostic accuracy and drug development efficiency. Especially in drug target discovery and antiviral peptide classification, AI technology can accelerate data processing and prediction, helping researchers identify potential therapeutic molecules more quickly and optimize the drug development process. This study proposes and validates a Deep learn-based model, deep-Avpiden, to improve the classification and discovery efficiency of antiviral peptides (AVPs). By using sequential convolutional networks (TCNs), the model outperforms traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in capturing long-term dependencies and parallel computing capabilities. In terms of dataset, we used AVPs and non-AVPS samples from multiple databases, totaling 5,414 cleaned and de-weighted peptide sequences for model training after data preprocessing and embedding. The Deep-AVPiden model has been shown to outperform existing advanced classifiers in experiments, and its effectiveness has been verified by accuracy, precision, recall rate, and area under the ROC curve (OC-ROC). In addition, to accommodate computing resource constraints, we propose an optimized version of Deep-AvPIDen (DS), which utilizes Deep separation convolution technology to significantly reduce computing resource consumption. Through the online application platform, researchers can efficiently classify antiviral proteins and discover new AVPs. Future research could further optimize the model's computational efficiency, handle larger data sets, and expand its potential for biomedical problems such as drug combination prediction and new drug discovery.
Keywords
Deep learning; Drug target discovery; Medical diagnosis; Antiviral peptide; Artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
