3D Convolutional Neural Networks for Predicting Protein Structure for Improved Drug Recommendation
DOI:
https://doi.org/10.4108/eetpht.10.5685Keywords:
Deep Learning, CNN, Convolutional Neural Networks, Protein StructureAbstract
INTRODUCTION: Protein structure prediction is critical for recommendation personalized medicine and drug discovery. This paper introduces a robust approach using 3D Convolution Neural Networks (3D CNN’s) to improve the accuracy of the structure of protein structure thus contributing for the drug recommendation system.
OBJECTIVES: In contrast to conventional techniques, 3D CNNs are able to identify complicated folding patterns and comprehend the subtle interactions between amino acids because they are able to capture spatial dependencies inside protein structures.
METHODS: Data sets are collected from Protein Data Bank, including experimental protein structures and the drugs that interact with them, are used to train the model. With the efficient processing of three-dimensional data, the 3D CNNs exhibit enhanced capability in identifying minute structural details that are crucial for drug binding. This drug recommendation system novel method makes it easier to find potential drugs that interact well with particular protein structures.
RESULTS: The performance of the proposed classifier is compared with the existing baseline methods with various parameters accuracy, precision, recall, F1 score, mean squared error (MSE) and area under the receiver operating characteristic curve (AUC-ROC).
CONCLUSION: Deep learning and 3D structural insights work together to create a new generation of tailored and focused therapeutic interventions by speeding up the drug development process and improving the accuracy of pharmacological recommendations.
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Hu, ShanShan, Chenglin Zhang, Peng Chen, Pengying Gu, Jun Zhang, and Bing Wang. "Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks." BMC bioinformatics 20 (2019): 1-12. DOI: https://doi.org/10.1186/s12859-019-3263-x
Jiménez, José, Stefan Doerr, Gerard Martínez-Rosell, Alexander S. Rose, and Gianni De Fabritiis. "DeepSite: protein-binding site predictor using 3D-convolutional neural networks." Bioinformatics 33, no. 19 (2017): 3036-3042. DOI: https://doi.org/10.1093/bioinformatics/btx350
Pu, Limeng, Rajiv Gandhi Govindaraj, Jeffrey Mitchell Lemoine, Hsiao-Chun Wu, and Michal Brylinski. "DeepDrug3D: classification of ligand-binding pockets in proteins with a convolutional neural network." PLoS computational biology 15, no. 2 (2019): e1006718. DOI: https://doi.org/10.1371/journal.pcbi.1006718
Wang, Xiao, Genki Terashi, Charles W. Christoffer, Mengmeng Zhu, and Daisuke Kihara. "Protein docking model evaluation by 3D deep convolutional neural networks." Bioinformatics 36, no. 7 (2020): 2113-2118. DOI: https://doi.org/10.1093/bioinformatics/btz870
Sato, Rin, and Takashi Ishida. "Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network." PloS one 14, no. 9 (2019): e0221347. DOI: https://doi.org/10.1371/journal.pone.0221347
Xu, Yinqiu, Hequan Yao, and Kejiang Lin. "An overview of neural networks for drug discovery and the inputs used." Expert opinion on drug discovery 13, no. 12 (2018): 1091-1102. DOI: https://doi.org/10.1080/17460441.2018.1547278
Pokkuluri, Kiran Sree, and SSSN Usha Devi Nedunuri. "A novel cellular automata classifier for covid-19 prediction." Journal of Health Sciences 10, no. 1 (2020): 34-38. DOI: https://doi.org/10.17532/jhsci.2020.907
Vaz, Joel Markus, and S. Balaji. "Convolutional neural networks (CNNs): Concepts and applications in pharmacogenomics." Molecular diversity 25, no. 3 (2021): 1569-1584. DOI: https://doi.org/10.1007/s11030-021-10225-3
Pagès, Guillaume, Benoit Charmettant, and Sergei Grudinin. "Protein model quality assessment using 3D oriented convolutional neural networks." Bioinformatics 35, no. 18 (2019): 3313-3319. DOI: https://doi.org/10.1093/bioinformatics/btz122
Kalakoti, Yogesh, Shashank Yadav, and Durai Sundar. "Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug–Target Interactions." ACS omega 7, no. 14 (2022): 12138-12146. DOI: https://doi.org/10.1021/acsomega.2c00424
Sree, P. Kiran, I. Ramesh Babu, and NSSSN Usha Devi. "Investigating an Artificial Immune System to strengthen protein structure prediction and protein coding region identification using the Cellular Automata classifier." International journal of bioinformatics research and applications 5, no. 6 (2009): 647-662. DOI: https://doi.org/10.1504/IJBRA.2009.029044
Diaz, Daniel J., Anastasiya V. Kulikova, Andrew D. Ellington, and Claus O. Wilke. "Using machine learning to predict the effects and consequences of mutations in proteins." Current Opinion in Structural Biology 78 (2023): 102518. DOI: https://doi.org/10.1016/j.sbi.2022.102518
Bordin, Nicola, Christian Dallago, Michael Heinzinger, Stephanie Kim, Maria Littmann, Clemens Rauer, Martin Steinegger, Burkhard Rost, and Christine Orengo. "Novel machine learning approaches revolutionize protein knowledge." Trends in Biochemical Sciences 48, no. 4 (2023): 345-359. DOI: https://doi.org/10.1016/j.tibs.2022.11.001
Stahl, Kolja, Andrea Graziadei, Therese Dau, Oliver Brock, and Juri Rappsilber. "Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning." Nature Biotechnology (2023): 1-10. DOI: https://doi.org/10.1038/s41587-023-01704-z
Chandra, Abel, Laura Tünnermann, Tommy Löfstedt, and Regina Gratz. "Transformer-based deep learning for predicting protein properties in the life sciences." Elife 12 (2023): e82819. DOI: https://doi.org/10.7554/eLife.82819
Szelogowski, Daniel. "Deep Learning for Protein Structure Prediction: Advancements in Structural Bioinformatics." bioRxiv (2023): 2023-04. DOI: https://doi.org/10.1101/2023.04.26.538026
Silpa, C., B. Sravani, D. Vinay, C. Mounika, and K. Poorvitha. "Drug Recommendation System in Medical Emergencies using Machine Learning." In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), pp. 107-112. IEEE, 2023. DOI: https://doi.org/10.1109/ICIDCA56705.2023.10099607
Pokkuluri, Kiran Sree, and Devi Nedunuri Usha. "A secure cellular automata integrated deep learning mechanism for health informatics." Int. Arab J. Inf. Technol. 18, no. 6 (2021): 782-788. DOI: https://doi.org/10.34028/iajit/18/6/5
Sellamuthu, Suseela, Srinivas Aditya Vaddadi, Srinivas Venkata, Hemant Petwal, Ravi Hosur, Vishwanadham Mandala, R. Dhanapal, and Jagendra singh. "AI-based recommendation model for effective decision to maximise ROI." Soft Computing (2023): 1-10.
Sellamuthu, Suseela, Srinivas Aditya Vaddadi, Srinivas Venkata, Hemant Petwal, Ravi Hosur, Vishwanadham Mandala, R. Dhanapal, and Jagendra singh. "AI-based recommendation model for effective decision to maximise ROI." Soft Computing (2023): 1-10. DOI: https://doi.org/10.1007/s00500-023-08731-7
Murugan, Suganiya, S. R. Srividhya, S. Pradeep Kumar, and B. Rubini. "A Machine Learning Approach to Predict Skin Diseases and Treatment Recommendation System." In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1157-1163. IEEE, 2023. DOI: https://doi.org/10.1109/ICSSIT55814.2023.10061150
Al Mubasher, Hadi, Ziad Doughan, Layth Sliman, and Ali Haidar. "A Novel Neural Network-Based Recommender System for Drug Recommendation." In International Conference on Engineering Applications of Neural Networks, pp. 573-584. Cham: Springer Nature Switzerland, 2023. DOI: https://doi.org/10.1007/978-3-031-34204-2_46
Fujihara, Kazuya, and Hirohito Sone. "Machine Learning Approach to Drug Treatment Strategy for Diabetes Care." Diabetes & Metabolism Journal 47, no. 3 (2023): 325-332. DOI: https://doi.org/10.4093/dmj.2022.0349
Sree, Pokkuluri Kiran, S. S. S. N. Usha Devi N, Phaneendra Varma Chintalapati, Gurujukota Ramesh Babu, and P. B. V. Raja Rao. "Drug Recommendations Using a Reviews and Sentiment Analysis by RNN." In International Conference on Cognitive Computing and Cyber Physical Systems, pp. 135-141. Cham: Springer Nature Switzerland, 2023. DOI: https://doi.org/10.1007/978-3-031-48888-7_11
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