计算机科学
人工智能
深度学习
卷积神经网络
重新使用
机器学习
过程(计算)
人工神经网络
药物靶点
药物发现
生物信息学
工程类
医学
废物管理
药理学
生物
操作系统
作者
R. Venkata Siva Reddy,M Khyathi,Niharika Mallikarjuna,M. Swetha
标识
DOI:10.1109/nmitcon58196.2023.10275870
摘要
The development of drug-target interactions (DTIs) is a decisive step for drug discovery and reuse process, because the effect of Antibiotics is now declining. Several methods have been proposed for this problem, but they rarely use a combination of protein and synthetic materials. In this paper, deep learning approach, and an easy-to-use library for DTI prediction using neural networks in learning, from proteins (amino acid sequences) properties of networks and Simplified Molecular Compounds Input line entry system (SMILES) array are used. The outcome shows that using convolutional neural network (CNN) instead of conventional Annotations to acquire statistics representations can enhance overall performance. The deep learning approach outperformed machine learning strategies in successfully classifying effective and interactions. The proposed approach uses BLASTP for protein sequence dataset, that contain real-global goal interaction records. The DTiGEMS+ tool is used for integrating various features of the drug and target. The proposed approach achieves 96% accuracy as compare to the existing drug prediction strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI