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Le NQK, Tran TX, Nguyen PA, Ho TT, Nguyen VN. Recent progress in machine learning approaches for predicting carcinogenicity in drug development. Expert Opin Drug Metab Toxicol 2024; 20:621-628. [PMID: 38742542 DOI: 10.1080/17425255.2024.2356162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 05/13/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Thi-Xuan Tran
- University of Economics and Business Administration, Thai Nguyen University, Thai Nguyen, Vietnam
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Vietnam
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Vietnam
| | - Trang-Thi Ho
- Department of Computer Science and Information Engineering, TamKang University, New Taipei, Taiwan
| | - Van-Nui Nguyen
- University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Vietnam
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Singh S, Zeh G, Freiherr J, Bauer T, Türkmen I, Grasskamp AT. Classification of substances by health hazard using deep neural networks and molecular electron densities. J Cheminform 2024; 16:45. [PMID: 38627862 PMCID: PMC11302296 DOI: 10.1186/s13321-024-00835-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/23/2024] [Indexed: 08/09/2024] Open
Abstract
In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage. Together with their 3-class electronegativity maps we train a modified 3D-UNet with electron density cubes to segment reactive sites in molecules and classify substances with an accuracy of 78.1%. We perform the same process on a custom food dataset (CompFood) consisting of hazardous and non-hazardous substances compiled from European Food Safety Authority (EFSA) OpenFoodTox, Food and Drug Administration (FDA) Generally Recognized as Safe (GRAS) and FooDB datasets to achieve a classification accuracy of 64.1%. Our results show that 3D electron densities and particularly masked electron densities, calculated by taking a product of original electron densities and regions of high and low electronegativity can be used to classify molecules for different use-cases and thus serve not only to guide safe-by-design product development but also aid in regulatory decisions. SCIENTIFIC CONTRIBUTION: We aim to contribute to the diverse 3D molecular representations used for training machine learning algorithms by showing that a deep learning network can be trained on 3D electron density representation of molecules. This approach has previously not been used to train machine learning models and it allows utilization of the true spatial domain of the molecule for prediction of properties such as their suitability for usage in cosmetics and food products and in future, to other molecular properties. The data and code used for training is accessible at https://github.com/s-singh-ivv/eDen-Substances .
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Affiliation(s)
- Satnam Singh
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Gina Zeh
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
| | - Jessica Freiherr
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Thilo Bauer
- Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052, Erlangen, Germany
| | - Isik Türkmen
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
| | - Andreas T Grasskamp
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany.
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Zhang H, Yi H, Hao Y, Zhao L, Pan W, Xue Q, Liu X, Fu J, Zhang A. Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133092. [PMID: 38039812 DOI: 10.1016/j.jhazmat.2023.133092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.
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Affiliation(s)
- Huazhou Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China
| | - Hang Yi
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China
| | - Yuxing Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Lu Zhao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Wenxiao Pan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Qiao Xue
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Jianjie Fu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China.
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Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Review of machine learning and deep learning models for toxicity prediction. Exp Biol Med (Maywood) 2023; 248:1952-1973. [PMID: 38057999 PMCID: PMC10798180 DOI: 10.1177/15353702231209421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
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Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Meng Song
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
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Chen Z, Zhang L, Sun J, Meng R, Yin S, Zhao Q. DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction. J Cell Mol Med 2023; 27:3117-3126. [PMID: 37525507 PMCID: PMC10568665 DOI: 10.1111/jcmm.17889] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/11/2023] [Accepted: 07/22/2023] [Indexed: 08/02/2023] Open
Abstract
The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.
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Affiliation(s)
- Zhe Chen
- School of Mathematics and StatisticsLiaoning UniversityShenyangChina
| | - Li Zhang
- School of Life ScienceLiaoning UniversityShenyangChina
| | - Jianqiang Sun
- School of Information Science and EngineeringLinyi UniversityLinyiChina
| | - Rui Meng
- School of Computer Science and Software EngineeringUniversity of Science and Technology LiaoningAnshanChina
| | - Shuaidong Yin
- School of Computer Science and Software EngineeringUniversity of Science and Technology LiaoningAnshanChina
| | - Qi Zhao
- School of Computer Science and Software EngineeringUniversity of Science and Technology LiaoningAnshanChina
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Wang B, Guo J, Liu X, Yu Y, Wu J, Wang Y. Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features. BMC Bioinformatics 2023; 24:338. [PMID: 37697256 PMCID: PMC10496404 DOI: 10.1186/s12859-023-05455-1] [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: 05/10/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features. RESULTS The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design. CONCLUSION Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.
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Affiliation(s)
- Binyou Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China
| | - Jianmin Guo
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Xiaofeng Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Yang Yu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Jianming Wu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
| | - Yiwei Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
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Mittal A, Ahuja G. Advancing chemical carcinogenicity prediction modeling: opportunities and challenges. Trends Pharmacol Sci 2023; 44:400-410. [PMID: 37183054 DOI: 10.1016/j.tips.2023.04.002] [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: 03/14/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023]
Abstract
Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.
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Affiliation(s)
- Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
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Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. J Chem Inf Model 2023; 63:2628-2643. [PMID: 37125780 DOI: 10.1021/acs.jcim.3c00200] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University - Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Agung Surya Wibowo
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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Hao N, Sun P, Zhao W, Li X. Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114806. [PMID: 36948010 DOI: 10.1016/j.ecoenv.2023.114806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 03/04/2023] [Accepted: 03/16/2023] [Indexed: 06/18/2023]
Abstract
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.
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Affiliation(s)
- Ning Hao
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Peixuan Sun
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Wenjin Zhao
- College of New Energy and Environment, Jilin University, Changchun 130012, China.
| | - Xixi Li
- State Environmental Protection Key Laboratory of Ecological Effect and Risk Assessment of Chemicals, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, A1B 3×5, Canada.
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Kour S, Biswas I, Sheoran S, Arora S, Sheela P, Duppala SK, Murthy DK, Pawar SC, Singh H, Kumar D, Prabhu D, Vuree S, Kumar R. Artificial intelligence and nanotechnology for cervical cancer treatment: Current status and future perspectives. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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A Graph Convolution Network with Subgraph Embedding for Mutagenic Prediction in Aromatic Hydrocarbons. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Limbu S, Dakshanamurthy S. Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218185. [PMID: 36365881 PMCID: PMC9653664 DOI: 10.3390/s22218185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/11/2022] [Accepted: 10/23/2022] [Indexed: 05/28/2023]
Abstract
Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.
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Abstract
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared to in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.
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Affiliation(s)
- Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
| | - Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
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14
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Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning. Pharmaceutics 2022; 14:pharmaceutics14050943. [PMID: 35631529 PMCID: PMC9143325 DOI: 10.3390/pharmaceutics14050943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 11/29/2022] Open
Abstract
Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy.
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15
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Abstract
Motivation Molecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carcinogenicity information is limited and building data-driven models with good prediction accuracy remains a major challenge. Results In this work, we propose CONCERTO, a deep learning model that uses a graph transformer in conjunction with a molecular fingerprint representation for carcinogenicity prediction from molecular structure. Special efforts have been made to overcome the data size constraint, such as multi-round pre-training on related but lower quality mutagenicity data, and transfer learning from a large self-supervised model. Extensive experiments demonstrate that our model performs well and can generalize to external validation sets. CONCERTO could be useful for guiding future carcinogenicity experiments and provide insight into the molecular basis of carcinogenicity. Availability and implementation The code and data underlying this article are available on github at https://github.com/bowang-lab/CONCERTO
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Affiliation(s)
- Philip Fradkin
- Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
- Vector Institute, Toronto, ON M5G 1M1, Canada
| | - Adamo Young
- Vector Institute, Toronto, ON M5G 1M1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Lazar Atanackovic
- Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
- Vector Institute, Toronto, ON M5G 1M1, Canada
| | | | - Leo J Lee
- To whom correspondence should be addressed. E-mail: or
| | - Bo Wang
- To whom correspondence should be addressed. E-mail: or
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16
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Li T, Tong W, Roberts R, Liu Z, Thakkar S. DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation. Front Artif Intell 2021; 4:757780. [PMID: 34870186 PMCID: PMC8636933 DOI: 10.3389/frai.2021.757780] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/27/2021] [Indexed: 12/16/2022] Open
Abstract
Carcinogenicity testing plays an essential role in identifying carcinogens in environmental chemistry and drug development. However, it is a time-consuming and label-intensive process to evaluate the carcinogenic potency with conventional 2-years rodent animal studies. Thus, there is an urgent need for alternative approaches to providing reliable and robust assessments on carcinogenicity. In this study, we proposed a DeepCarc model to predict carcinogenicity for small molecules using deep learning-based model-level representations. The DeepCarc Model was developed using a data set of 692 compounds and evaluated on a test set containing 171 compounds in the National Center for Toxicological Research liver cancer database (NCTRlcdb). As a result, the proposed DeepCarc model yielded a Matthews correlation coefficient (MCC) of 0.432 for the test set, outperforming four advanced deep learning (DL) powered quantitative structure-activity relationship (QSAR) models with an average improvement rate of 37%. Furthermore, the DeepCarc model was also employed to screen the carcinogenicity potential of the compounds from both DrugBank and Tox21. Altogether, the proposed DeepCarc model could serve as an early detection tool (https://github.com/TingLi2016/DeepCarc) for carcinogenicity assessment.
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Affiliation(s)
- Ting Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States,University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Joint Bioinformatics Program, Little Rock, AR, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States
| | - Ruth Roberts
- ApconiX Ltd., Alderley Edge, United Kingdom,Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States,*Correspondence: Zhichao Liu, ; Shraddha Thakkar,
| | - Shraddha Thakkar
- Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States,*Correspondence: Zhichao Liu, ; Shraddha Thakkar,
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17
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Wang Y, Wang B, Jiang J, Guo J, Lai J, Lian XY, Wu J. Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants. ACS OMEGA 2021; 6:26545-26555. [PMID: 34661009 PMCID: PMC8515573 DOI: 10.1021/acsomega.1c03842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/14/2021] [Indexed: 05/17/2023]
Abstract
Drug development has a high failure rate, with safety properties constituting a considerable challenge. To reduce risk, in silico tools, including various machine learning methods, have been applied for toxicity prediction. However, these approaches often confront a serious problem: the training data sets are usually biased (imbalanced positive and negative samples), which would result in model training difficulty and unsatisfactory prediction accuracy. Multitask networks obtained significantly better predictive accuracies than single-task methods, and capsule neural networks showed excellent performance in sparse data sets in previous studies. In this study, we developed a new multitask framework based on a capsule neural network (multitask CapsNet) to measure 12 different toxic effects simultaneously. We found that multitask CapsNet excelled in toxicity prediction and outperformed many other computational approaches using the multitask strategy. Only after training on biased data sets did multitask CapsNet achieve significantly improved prediction accuracy on the Tox21 Data Challenge, which gave the largest ratio of highest accuracy (8/12) among compared models. Our model gave a prediction accuracy of 96.6% for the target NR.PPAR.gamma, whose ratio of negative to positive samples was up to 36:1. These results suggested that multitask CapsNet could overcome the bias problems and would provide a novel, accurate, and efficient approach for predicting the toxicities of compounds.
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Affiliation(s)
- Yiwei Wang
- School
of Preclinical Medicine, Southwest Medical
University, Luzhou 646000, China
| | - Binyou Wang
- School
of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Jie Jiang
- School
of Preclinical Medicine, Southwest Medical
University, Luzhou 646000, China
| | - Jianmin Guo
- School
of Preclinical Medicine, Southwest Medical
University, Luzhou 646000, China
| | - Jia Lai
- School
of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Xiao-Yuan Lian
- School
of Pharmacy, Zhejiang University, Hangzhou 310011, China
| | - Jianming Wu
- Key
Laboratory of Medical Electrophysiology, Ministry of Education of
China, Medical Key Laboratory for Drug Discovery and Druggability
Evaluation of Sichuan Province, Luzhou Key
Laboratory of Activity Screening and Druggability Evaluation for Chinese
Materia Medica, Luzhou 646000, China
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18
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Venkatraman V. FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminform 2021; 13:75. [PMID: 34583740 PMCID: PMC8479898 DOI: 10.1186/s13321-021-00557-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/20/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. AVAILABILITY The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .
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Affiliation(s)
- Vishwesh Venkatraman
- Norwegian University of Science and Technology, Realfagbygget, Gløshaugen, Høgskoleringen, 7491, Trondheim, Norway.
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19
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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20
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Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19:4538-4558. [PMID: 34471498 PMCID: PMC8387781 DOI: 10.1016/j.csbj.2021.08.011] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 12/30/2022] Open
Abstract
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
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Key Words
- ADMET, Absorption, distribution, metabolism, elimination and toxicity
- ADR, Adverse Drug Reaction
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APFP, Atom Pairs 2d FingerPrint
- AUC, Area under the Curve
- BBB, Blood–Brain barrier
- CDK, Chemical Development Kit
- CNN, Convolutional Neural Networks
- CNS, Central Nervous System
- CPI, Compound-protein interaction
- CV, Cross Validation
- Cheminformatics
- DL, Deep Learning
- DNA, Deoxyribonucleic acid
- Deep Learning
- Drug Discovery
- ECFP, Extended Connectivity Fingerprints
- FDA, Food and Drug Administration
- FNN, Fully Connected Neural Networks
- FP, Fringerprints
- FS, Feature Selection
- GCN, Graph Convolutional Networks
- GEO, Gene Expression Omnibus
- GNN, Graph Neural Networks
- GO, Gene Ontology
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- MACCS, Molecular ACCess System
- MCC, Matthews correlation coefficient
- MD, Molecular Descriptors
- MKL, Multiple Kernel Learning
- ML, Machine Learning
- Machine Learning
- Molecular Descriptors
- NB, Naive Bayes
- OOB, Out of Bag
- PCA, Principal Component Analyisis
- QSAR
- QSAR, Quantitative structure–activity relationship
- RF, Random Forest
- RNA, Ribonucleic Acid
- SMILES, simplified molecular-input line-entry system
- SVM, Support Vector Machines
- TCGA, The Cancer Genome Atlas
- WHO, World Health Organization
- t-SNE, t-Distributed Stochastic Neighbor Embedding
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Jose Liñares-Blanco
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
| | - Nereida Rodríguez-Fernández
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco Cedrón
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco J. Novoa
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Adrian Carballal
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Victor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, Madrid 28660, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
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21
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Jiao Z, Hu P, Xu H, Wang Q. Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications. ACS CHEMICAL HEALTH & SAFETY 2020. [DOI: 10.1021/acs.chas.0c00075] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Zeren Jiao
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Pingfan Hu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Hongfei Xu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Qingsheng Wang
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
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