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Paremskaia AI, Rudik AV, Filimonov DA, Lagunin AA, Poroikov VV, Tarasova OA. Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets. Viruses 2023; 15:2245. [PMID: 38005921 PMCID: PMC10674809 DOI: 10.3390/v15112245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.
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Affiliation(s)
- Anastasiia Iu. Paremskaia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia;
- Live Sciences Research Center, Moscow Institute of Physics and Technology, National Research University, Institutsky Lane 9, Dolgoprudny 141700, Russia
| | - Anastassia V. Rudik
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Dmitry A. Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Alexey A. Lagunin
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia;
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Vladimir V. Poroikov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
| | - Olga A. Tarasova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow 119121, Russia; (A.V.R.); (D.A.F.); (V.V.P.)
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Chen S, Li T, Yang L, Zhai F, Jiang X, Xiang R, Ling G. Artificial intelligence-driven prediction of multiple drug interactions. Brief Bioinform 2022; 23:6720429. [PMID: 36168896 DOI: 10.1093/bib/bbac427] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 12/14/2022] Open
Abstract
When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.
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Affiliation(s)
- Siqi Chen
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Tiancheng Li
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Luna Yang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Fei Zhai
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Xiwei Jiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Rongwu Xiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China.,Liaoning Medical Big Data and Artificial Intelligence Engineering Technology Research Center, Shenyang 110016, China
| | - Guixia Ling
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
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Daniel J, Irin Sherly S, Ponnuramu V, Pratap Singh D, Netra SN, Alonazi WB, Almutairi KMA, Priyan KSA, Abera Y. Recurrent Neural Networks for Feature Extraction from Dengue Fever. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:5669580. [PMID: 35722151 PMCID: PMC9203200 DOI: 10.1155/2022/5669580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/29/2022] [Indexed: 11/22/2022]
Abstract
Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.
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Affiliation(s)
- Jackson Daniel
- Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, Nallatinputhur, Tamil Nadu 628503, India
| | - S. Irin Sherly
- Department of Information Technology, Panimalar Institute of Technology, Chennai, Tamil Nadu 600123, India
| | - Veeralakshmi Ponnuramu
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 600124, India
| | - Devesh Pratap Singh
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand 248002, India
| | - S. N. Netra
- Department of Information Science and Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka 560049, India
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, P. O. Box: 71115, Riyadh 11587, Saudi Arabia
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box: 10219, Riyadh 11433, Saudi Arabia
| | - K. S. A. Priyan
- Department of Biotechnology, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Yared Abera
- Department of Technology and Informatics, Ambo University, Woliso Campus, Ambo, Ethiopia
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