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Chen L, Li B, Chen Y, Lin M, Zhang S, Li C, Pang Y, Wang L. ADCNet: a unified framework for predicting the activity of antibody-drug conjugates. Brief Bioinform 2025; 26:bbaf228. [PMID: 40421657 DOI: 10.1093/bib/bbaf228] [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: 12/10/2024] [Revised: 02/25/2025] [Accepted: 04/28/2025] [Indexed: 05/28/2025] Open
Abstract
Antibody-drug conjugates (ADCs) have revolutionized the field of cancer treatment in the era of precision medicine due to their ability to precisely target cancer cells and release highly effective drugs. Nevertheless, the rational design and discovery of ADCs remain challenging because the relationship between their quintuple structures and activities is difficult to explore and understand. To address this issue, we first introduce a unified deep learning framework called ADCNet to explore such relationship and help design potential ADCs. The ADCNet highly integrates the protein representation learning language model ESM-2 and small-molecule representation learning language model functional group-based bidirectional encoder representations from transformers to achieve activity prediction through learning meaningful features from antigen and antibody protein sequences of ADC, SMILES strings of linker and payload, and drug-antibody ratio (DAR) value. Based on a carefully designed and manually tailored ADC data set, extensive evaluation results reveal that ADCNet performs best on the test set compared to baseline machine learning models across all evaluation metrics. For example, it achieves an average prediction accuracy of 87.12%, a balanced accuracy of 0.8689, and an area under receiver operating characteristic curve of 0.9293 on the test set. In addition, cross-validation, ablation experiments, and external independent testing results further prove the stability, advancement, and robustness of the ADCNet architecture. For the convenience of the community, we develop the first online platform (https://ADCNet.idruglab.cn) for the prediction of ADCs activity based on the optimal ADCNet model, and the source code is publicly available at https://github.com/idrugLab/ADCNet.
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Affiliation(s)
- Liye Chen
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Biaoshun Li
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Yihao Chen
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Mujie Lin
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Shipeng Zhang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Chenxin Li
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Yu Pang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, China
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Bhattacharjee A, Kumar A, Ojha PK, Kar S. Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design. Expert Opin Drug Discov 2025; 20:621-641. [PMID: 40241626 DOI: 10.1080/17460441.2025.2491669] [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: 11/10/2024] [Accepted: 04/07/2025] [Indexed: 04/18/2025]
Abstract
INTRODUCTION Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs often leads to adverse drug reactions (ADRs) and therapeutic failure. As such, early prediction of such inhibitors is vital in drug development. In this context, the limitations of the traditional in vitro assays and QSAR models methods have been addressed by harnessing artificial intelligence (AI) techniques. AREAS COVERED This narrative review presents the insights gained from the application of AI for predicting DME and DT inhibitors over the past decade. Several case studies demonstrate successful AI applications in enzyme-transporter interaction prediction, and the authors discuss workflows for integrating these predictions into drug design and regulatory frameworks. EXPERT OPINION The application of AI in predicting DME and DT inhibitors has demonstrated significant potential toward enhancing drug safety and effectiveness. However, critical challenges involve the data quality, biases, and model transparency. The availability of diverse, high-quality datasets alongside the integration of pharmacokinetic and genomic data are essential. Lastly, the collaboration among computational scientists, pharmacologists, and regulatory bodies is pyramidal in tailoring AI tools for personalized medicine and safer drug development.
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Affiliation(s)
- Arnab Bhattacharjee
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Ankur Kumar
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry and Physics, Kean University, Union, NJ, USA
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3
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Permadi EE, Watanabe R, Mizuguchi K. Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning. J Cheminform 2025; 17:66. [PMID: 40307863 PMCID: PMC12044814 DOI: 10.1186/s13321-025-01015-2] [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: 01/07/2025] [Accepted: 04/13/2025] [Indexed: 05/02/2025] Open
Abstract
The cytochrome P450 (CYP) superfamily metabolises a wide range of compounds; however, drug-induced CYP inhibition can lead to adverse interactions. Identifying potential CYP inhibitors is crucial for safe drug administration. This study investigated the application of deep learning techniques to the prediction of CYP inhibition, focusing on the challenges posed by limited datasets for CYP2B6 and CYP2C8 isoforms. To tackle these limitations, we leveraged larger datasets for related CYP isoforms, compiling comprehensive data from public databases containing IC50 values for 12,369 compounds that target seven CYP isoforms. We constructed single-task, fine-tuning, multitask, and multitask models incorporating data imputation on the missing values. Notably, the multitask models with data imputation demonstrated significant improvement in CYP inhibition prediction over the single-task models. Using the most accurate prediction models, we evaluated the inhibitory activity of approved drugs against CYP2B6 and CYP2C8. Among the 1,808 approved drugs analysed, our multitask models with data imputation identified 161 and 154 potential inhibitors of CYP2B6 and CYP2C8, respectively. This study underscores the significant potential of multitask deep learning, particularly when utilising a graph convolutional network with data imputation, to enhance the accuracy of CYP inhibition predictions under the conditions of limited data availability.Scientific contributionThis study demonstrates that even with small datasets, accurate prediction models can be constructed by utilising related data effectively. Also, our imputation techniques on the missing values improved the prediction accuracy of CYP2B6 and CYP2C8 inhibition significantly.
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Affiliation(s)
- Elpri Eka Permadi
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency (BRIN), Meatpro Building, Kawasan Sains dan Teknologi (KST) Soekarno, Jl. Raya Jakarta-Bogor KM 46, Cibinong, Jawa Barat, 16911, Indonesia
| | - Reiko Watanabe
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Kenji Mizuguchi
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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4
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Han H, Shaker B, Lee JH, Choi S, Yoon S, Singh M, Basith S, Cui M, Ahn S, An J, Kang S, Yeom MS, Choi S. Employing Automated Machine Learning (AutoML) Methods to Facilitate the In Silico ADMET Properties Prediction. J Chem Inf Model 2025; 65:3215-3225. [PMID: 40085003 PMCID: PMC12004515 DOI: 10.1021/acs.jcim.4c02122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2024] [Revised: 03/07/2025] [Accepted: 03/07/2025] [Indexed: 03/16/2025]
Abstract
The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in in silico ADMET modeling has gained momentum due to its high-throughput and low-cost attributes. In this study, we developed a machine learning model capable of predicting 11 ADMET properties of chemical compounds. Each model was constructed by combining one of 40 classification algorithms including random forest (RF), extreme gradient boosting (XGB), support vector machine (SVM), and gradient boosting (GB) with one of three predefined hyperparameter configurations. This process can be efficiently performed using automated machine learning (AutoML) methods, which automatically search for the best combination of model algorithms and optimized hyperparameters. We developed optimal predictive models for 11 different ADMET properties using the Hyperopt-sklearn AutoML method. All of the developed models depicted an area under the ROC curve (AUC) >0.8. Furthermore, our developed models outperformed most of the ADMET properties and showed comparable performance in other properties when evaluated on external data sets and compared with published predictive models. Our results support the applicability of AutoML in ADMET prediction and will be helpful for ADMET prediction in early-stage drug discovery.
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Affiliation(s)
- Herim Han
- NamuICT
R&D Center, NamuICT, Seoul 07793, Republic
of Korea
| | - Bilal Shaker
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Jin Hee Lee
- Ildong
Pharmaceutical Co. Ltd., Hwaseong 18449, Republic of Korea
| | - Sunghwan Choi
- Department
of Chemistry, Inha University, 100 Inha-ro, Incheon 22212, Michuhol-gu, Republic of Korea
| | - Sanghee Yoon
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Maninder Singh
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Shaherin Basith
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Minghua Cui
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Sunil Ahn
- Korea
Institute of Science and Technology Information, Daejeon 34141, Republic of Korea
| | - Junyoung An
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Soosung Kang
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
| | - Min Sun Yeom
- NamuICT
R&D Center, NamuICT, Seoul 07793, Republic
of Korea
| | - Sun Choi
- Global
AI Drug Discovery Center, College of Pharmacy and Graduate School
of Pharmaceutical Sciences, Ewha Womans
University, Seoul 03760, Republic of Korea
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Zhao D, Zhang Y, Chen Y, Li B, Zhou W, Wang L. Highly Accurate and Explainable Predictions of Small-Molecule Antioxidants for Eight In Vitro Assays Simultaneously through an Alternating Multitask Learning Strategy. J Chem Inf Model 2024; 64:9098-9110. [PMID: 38888465 DOI: 10.1021/acs.jcim.4c00748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.
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Affiliation(s)
- Duancheng Zhao
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yanhong Zhang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yihao Chen
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Biaoshun Li
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Wenguang Zhou
- Central Laboratory of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan 528200, China
| | - Ling Wang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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6
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Wei Y, Palazzolo L, Ben Mariem O, Bianchi D, Laurenzi T, Guerrini U, Eberini I. Investigation of in silico studies for cytochrome P450 isoforms specificity. Comput Struct Biotechnol J 2024; 23:3090-3103. [PMID: 39188968 PMCID: PMC11347072 DOI: 10.1016/j.csbj.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/28/2024] Open
Abstract
Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.
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Affiliation(s)
- Yao Wei
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Luca Palazzolo
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Omar Ben Mariem
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Davide Bianchi
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Tommaso Laurenzi
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Uliano Guerrini
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Ivano Eberini
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
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7
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Ambe K, Nakamori M, Tohno R, Suzuki K, Sasaki T, Tohkin M, Yoshinari K. Machine Learning-Based In Silico Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s. Chem Res Toxicol 2024; 37:1843-1850. [PMID: 39427263 DOI: 10.1021/acs.chemrestox.4c00168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2024]
Abstract
The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid in silico tool. However, there are few in silico models focusing on the species differences between rat and human in the P450s inhibition. This study aimed to establish in silico models to classify chemical substances as inhibitors or non-inhibitors of various rat and human P450s, using only molecular descriptors. Using the in-house test results from our in vitro experiments, we used 326 substances for model construction and internal validation data. Apart from the 326 substances, 60 substances were used as external validation data set. We focused on seven rat P450s (CYP1A1, CYP1A2, CYP2B1, CYP2C6, CYP2D1, CYP2E1, and CYP3A2) and 11 human P450s (CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4). Most of the models established using XGBoost showed an area under the receiver operating characteristic curve (ROC-AUC) of 0.8 or more in the internal validation. When we set an applicability domain for the models and confirmed their generalization performance through external validation, most of the models showed an ROC-AUC of 0.7 or more. Interestingly, for CYP1A1 and CYP1A2, we discovered that a human P450 inhibitory activity model can predict rat P450 inhibitory activity and vice versa. These models are the first attempts to predict inhibitory activity against a wide variety of P450s in both rats and humans using chemical structure information. Our experimental results and in silico models would be helpful to support information for species similarities and differences in chemical-induced toxicity.
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Affiliation(s)
- Kaori Ambe
- Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan
| | - Mizuki Nakamori
- Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan
| | - Riku Tohno
- Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan
| | - Kotaro Suzuki
- Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan
| | - Takamitsu Sasaki
- Laboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 4228526, Japan
| | - Masahiro Tohkin
- Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan
| | - Kouichi Yoshinari
- Laboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 4228526, Japan
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8
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Lin M, Cai J, Wei Y, Peng X, Luo Q, Li B, Chen Y, Wang L. MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery. Eur J Med Chem 2024; 277:116776. [PMID: 39173285 DOI: 10.1016/j.ejmech.2024.116776] [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: 05/11/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/24/2024]
Abstract
Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in accelerating antimalarial drug discovery, the performance of deep learning models based on molecular graph and other co-representation approaches warrants further exploration. Current research has overlooked mutant strains of the malaria parasite with varying degrees of sensitivity or resistance, and has not covered the prediction of inhibitory activities across the three major life cycle stages (liver, asexual blood, and gametocyte) within the human host, which is crucial for both treatment and transmission blocking. In this study, we manually curated a benchmark antimalarial activity dataset comprising 407,404 unique compounds and 410,654 bioactivity data points across ten Plasmodium phenotypes and three stages. The performance was systematically compared among two fingerprint-based ML models (RF::Morgan and XGBoost:Morgan), four graph-based DL models (GCN, GAT, MPNN, and Attentive FP), and three co-representations DL models (FP-GNN, HiGNN, and FG-BERT), which reveal that: 1) The FP-GNN model achieved the best predictive performance, outperforming the other methods in distinguishing active and inactive compounds across balanced, more positive, and more negative datasets, with an overall AUROC of 0.900; 2) Fingerprint-based ML models outperformed graph-based DL models on large datasets (>1000 compounds), but the three co-representations DL models were able to incorporate domain-specific chemical knowledge to bridge this gap, achieving better predictive performance. These findings provide valuable guidance for selecting appropriate ML and DL methods for antimalarial activity prediction tasks. The interpretability analysis of the FP-GNN model revealed its ability to accurately capture the key structural features responsible for the liver- and blood-stage activities of the known antimalarial drug atovaquone. Finally, we developed a web server, MalariaFlow, incorporating these high-quality models for antimalarial activity prediction, virtual screening, and similarity search, successfully predicting novel triple-stage antimalarial hits validated through experimental testing, demonstrating its effectiveness and value in discovering potential multistage antimalarial drug candidates.
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Affiliation(s)
- Mujie Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Junxi Cai
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510006, China
| | - Yuancheng Wei
- School of Software Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Xinru Peng
- School of Software Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Qianhui Luo
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Biaoshun Li
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yihao Chen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Ling Wang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China.
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9
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Zonyfar C, Ngnamsie Njimbouom S, Mosalla S, Kim JD. GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors. J Cheminform 2024; 16:119. [PMID: 39472986 PMCID: PMC11524008 DOI: 10.1186/s13321-024-00915-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 10/10/2024] [Indexed: 11/02/2024] Open
Abstract
State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction.Scientific contributionThe prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs .
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Affiliation(s)
- Candra Zonyfar
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | | | - Sophia Mosalla
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Jeong-Dong Kim
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan, 31460, Republic of Korea.
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea.
- Genome-based BioIT Convergence Institute, Sun Moon University, Asan, 31460, Republic of Korea.
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10
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Fang J, Tang Y, Gong C, Huang Z, Feng Y, Liu G, Tang Y, Li W. Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models. Chem Res Toxicol 2024; 37:1535-1548. [PMID: 39196814 DOI: 10.1021/acs.chemrestox.4c00199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2024]
Abstract
Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the clinically used drugs. P450-mediated metabolism is also closely associated with the formation of toxic metabolites and drug-drug interactions. Therefore, it is of high importance to predict if a compound is the substrate of a given P450 in the early stage of drug development. In this study, we built the multitask learning models to simultaneously predict the substrates of five major drug-metabolizing P450 enzymes, namely, CYP3A4, 2C9, 2C19, 2D6, and 1A2, based on the collected substrate data sets. Compared to the single-task model and conventional machine learning models, the multitask fingerprints and graph neural networks model achieved superior performance with the average AUC values of 90.8% on the test set. Notably, the multitask model demonstrated its good performance on the small amount of substrate data sets such as CYP1A2, 2C9, and 2C19. In addition, the Shapley additive explanation and the attention mechanism were used to reveal specific substructures associated with P450 substrates, which were further confirmed and complemented by the substructure mining tool and the literature.
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Affiliation(s)
- Jiaojiao Fang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yan Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Changda Gong
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zejun Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yanjun Feng
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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11
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Boonyarit B, Yamprasert N, Kaewnuratchadasorn P, Kinchagawat J, Prommin C, Rungrotmongkol T, Nutanong S. GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity. J Comput Chem 2024; 45:2001-2023. [PMID: 38713612 DOI: 10.1002/jcc.27388] [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: 01/08/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/09/2024]
Abstract
The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.
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Affiliation(s)
- Bundit Boonyarit
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Nattawin Yamprasert
- School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | | | - Jiramet Kinchagawat
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Chanatkran Prommin
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Sarana Nutanong
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
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12
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Zahid H, Tayara H, Chong KT. Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties. Arch Toxicol 2024; 98:2647-2658. [PMID: 38619593 DOI: 10.1007/s00204-024-03756-9] [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: 02/23/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.
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Affiliation(s)
- Hamza Zahid
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
- Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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13
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Arav Y. Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models. Pharmaceutics 2024; 16:978. [PMID: 39204323 PMCID: PMC11359797 DOI: 10.3390/pharmaceutics16080978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/17/2024] [Accepted: 07/22/2024] [Indexed: 09/04/2024] Open
Abstract
Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.
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Affiliation(s)
- Yehuda Arav
- Department of Applied Mathematics, Israeli Institute for Biological Research, P.O. Box 19, Ness-Ziona 7410001, Israel
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14
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Hossam Abdelmonem B, Abdelaal NM, Anwer EKE, Rashwan AA, Hussein MA, Ahmed YF, Khashana R, Hanna MM, Abdelnaser A. Decoding the Role of CYP450 Enzymes in Metabolism and Disease: A Comprehensive Review. Biomedicines 2024; 12:1467. [PMID: 39062040 PMCID: PMC11275228 DOI: 10.3390/biomedicines12071467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 07/28/2024] Open
Abstract
Cytochrome P450 (CYP450) is a group of enzymes that play an essential role in Phase I metabolism, with 57 functional genes classified into 18 families in the human genome, of which the CYP1, CYP2, and CYP3 families are prominent. Beyond drug metabolism, CYP enzymes metabolize endogenous compounds such as lipids, proteins, and hormones to maintain physiological homeostasis. Thus, dysregulation of CYP450 enzymes can lead to different endocrine disorders. Moreover, CYP450 enzymes significantly contribute to fatty acid metabolism, cholesterol synthesis, and bile acid biosynthesis, impacting cellular physiology and disease pathogenesis. Their diverse functions emphasize their therapeutic potential in managing hypercholesterolemia and neurodegenerative diseases. Additionally, CYP450 enzymes are implicated in the onset and development of illnesses such as cancer, influencing chemotherapy outcomes. Assessment of CYP450 enzyme expression and activity aids in evaluating liver health state and differentiating between liver diseases, guiding therapeutic decisions, and optimizing drug efficacy. Understanding the roles of CYP450 enzymes and the clinical effect of their genetic polymorphisms is crucial for developing personalized therapeutic strategies and enhancing drug responses in diverse patient populations.
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Affiliation(s)
- Basma Hossam Abdelmonem
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (B.H.A.); (M.A.H.); (Y.F.A.); (R.K.); (M.M.H.)
- Department of Microbiology and Immunology, Faculty of Pharmacy, October University for Modern Sciences & Arts (MSA), Giza 12451, Egypt
| | - Noha M. Abdelaal
- Biotechnology Graduate Program, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (N.M.A.); (E.K.E.A.); (A.A.R.)
| | - Eman K. E. Anwer
- Biotechnology Graduate Program, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (N.M.A.); (E.K.E.A.); (A.A.R.)
- Department of Microbiology and Immunology, Faculty of Pharmacy, Modern University for Technology and Information, Cairo 4411601, Egypt
| | - Alaa A. Rashwan
- Biotechnology Graduate Program, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (N.M.A.); (E.K.E.A.); (A.A.R.)
| | - Mohamed Ali Hussein
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (B.H.A.); (M.A.H.); (Y.F.A.); (R.K.); (M.M.H.)
| | - Yasmin F. Ahmed
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (B.H.A.); (M.A.H.); (Y.F.A.); (R.K.); (M.M.H.)
| | - Rana Khashana
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (B.H.A.); (M.A.H.); (Y.F.A.); (R.K.); (M.M.H.)
| | - Mireille M. Hanna
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (B.H.A.); (M.A.H.); (Y.F.A.); (R.K.); (M.M.H.)
| | - Anwar Abdelnaser
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt; (B.H.A.); (M.A.H.); (Y.F.A.); (R.K.); (M.M.H.)
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15
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Gong C, Feng Y, Zhu J, Liu G, Tang Y, Li W. Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition. J Appl Toxicol 2024; 44:1050-1066. [PMID: 38544296 DOI: 10.1002/jat.4601] [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: 01/12/2024] [Revised: 02/26/2024] [Accepted: 03/05/2024] [Indexed: 07/21/2024]
Abstract
Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC) of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition.
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Affiliation(s)
- Changda Gong
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yanjun Feng
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jieyu Zhu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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16
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Zhao D, Huang P, Yu L, He Y. Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges. Clin Pharmacokinet 2024; 63:919-944. [PMID: 38888813 DOI: 10.1007/s40262-024-01391-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2024] [Indexed: 06/20/2024]
Abstract
Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.
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Affiliation(s)
- Di Zhao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Ping Huang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China
| | - Li Yu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China.
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17
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Chen Z, Zhang L, Zhang P, Guo H, Zhang R, Li L, Li X. Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition. J Chem Inf Model 2024; 64:2528-2538. [PMID: 37864562 DOI: 10.1021/acs.jcim.3c01396] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2023]
Abstract
Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry. In the past few decades, many computational models have been reported, and some provided good performance. However, there are still several issues that should be resolved for these models, such as single isoform, models with unbalanced performance, lack of structural characteristics analysis, and poor availability. In the present study, the deep learning models based on python using the Keras framework and TensorFlow were developed for the chemical inhibition of each CYP isoform. These models were established based on a large data set containing 85715 compounds extracted from the PubChem bioassay database. On external validation, the models provided good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and the codes for the model were made open source in the Supporting Information. In addition, we also analyzed the structural characteristics of chemicals with CYP450 inhibition and detected the structural alerts (SAs), which should be responsible for the inhibition. The SAs were also made available online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful tool for the prediction of CYP450 inhibitors, and the SAs should provide useful information for the mechanisms of Cytochrome P450 inhibition.
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Affiliation(s)
- Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Le Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Ling Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
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18
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Almehizia AA, Aboulthana WM, Naglah AM, Hassan AS. In vitro biological studies and computational prediction-based analyses of pyrazolo[1,5- a]pyrimidine derivatives. RSC Adv 2024; 14:8397-8408. [PMID: 38476172 PMCID: PMC10928850 DOI: 10.1039/d4ra00423j] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/24/2024] [Indexed: 03/14/2024] Open
Abstract
There is a need for new pharmaceutical discoveries from bioactive nitrogenous derivatives due to the emergence of scourges, numerous pandemics, and diverse health problems. In this context, pyrazolo[1,5-a]pyrimidine derivatives 12a and 12b were synthesized and screened to evaluate their biological potentials in vitro as antioxidants, anti-diabetics, anti-Alzheimer's, anti-arthritics, and anti-cancer agents. Additionally, the computational pharmacokinetic and toxicity properties of the two pyrazolo[1,5-a]pyrimidines 12a and 12b were calculated and analyzed. The preliminary studies and results of this work represent the initial steps toward more advanced studies and define the bioactive chemical structure of pyrazolo[1,5-a]pyrimidine derivatives with the goal of exploring new drugs to address numerous health problems.
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Affiliation(s)
- Abdulrahman A Almehizia
- Drug Exploration & Development Chair (DEDC), Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University Riyadh 11451 Saudi Arabia
| | - Wael M Aboulthana
- Biochemistry Department, Biotechnology Research Institute, National Research Centre Dokki 12662 Cairo Egypt
| | - Ahmed M Naglah
- Drug Exploration & Development Chair (DEDC), Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University Riyadh 11451 Saudi Arabia
| | - Ashraf S Hassan
- Organometallic and Organometalloid Chemistry Department, National Research Centre Dokki 12622 Cairo Egypt
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19
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Wiraswati HL, Bashari MH, Alfarafisa NM, Ma’ruf IF, Sholikhah EN, Wahyuningsih TD, Satriyo PB, Mustofa M, Satria D, Damayanti E. Pyrazoline B-Paclitaxel or Doxorubicin Combination Drugs Show Synergistic Activity Against Cancer Cells: In silico Study. Adv Appl Bioinform Chem 2024; 17:33-46. [PMID: 38435441 PMCID: PMC10908341 DOI: 10.2147/aabc.s452281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/10/2024] [Indexed: 03/05/2024] Open
Abstract
Background Multidrug resistance in various cancer types is a major obstacle in cancer treatment. The concept of a single drug molecular target often causes treatment failure due to the complexity of the cellular processes. Therefore, combination chemotherapy, in which two or more anticancer drugs are co-administered, can overcome this problem because it potentially have synergistic efficacy besides reducing resistance, and drug doses. Previously, we reported that pyrazoline B had promising anticancer activity in both in silico and in vitro studies. To increase the efficacy of this drug, co-administration with established anticancer drugs such as doxorubicin and paclitaxel is necessary. Materials and Methods In this study, we used an in silico approach to predict the synergistic effect of pyrazoline B with paclitaxel or doxorubicin using various computational frameworks and compared the results with those of an established study on the combination of doxorubicin-cyclophosphamide and paclitaxel-ascorbic acid. Results and Discussion Drug interaction analysis showed the combination was safe with no contraindications or side effects. Furthermore, molecular docking studies revealed that doxorubicin-pyrazoline B and doxorubicin-cyclophosphamide may synergistically inhibit cancer cell proliferation by inhibiting the binding of topoisomerase I to the DNA chain. Moreover, the combination of pyrazoline B-paclitaxel may has synergistic activity to cause apoptosis by inhibiting Bcl2 binding to the Bax fragment or inhibiting cell division by inhibiting α-β tubulin disintegration. Paclitaxel-ascorbic acid had a synergistic effect on the inhibition of α-β tubulin disintegration. Conclusion The results show that this combination is promising for further in vitro and in vivo studies.
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Affiliation(s)
- Hesti Lina Wiraswati
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
- Oncology and Stem Cells Working Group, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Muhammad Hasan Bashari
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
- Oncology and Stem Cells Working Group, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Nayla Majeda Alfarafisa
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
- Oncology and Stem Cells Working Group, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Ilma Fauziah Ma’ruf
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency, Bogor, Indonesia
| | - Eti Nurwening Sholikhah
- Department of Pharmacology and Therapy, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Tutik Dwi Wahyuningsih
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Pamungkas Bagus Satriyo
- Department of Pharmacology and Therapy, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Mustofa Mustofa
- Department of Pharmacology and Therapy, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Denny Satria
- Department of Pharmaceutical Biology, Faculty of Pharmacy, Universitas Sumatera Utara, Medan, Indonesia
| | - Ema Damayanti
- Research Center for Food Technology and Processing, National Research and Innovation Agency, Gunungkidul, Indonesia
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20
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Dudas B, Miteva MA. Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends Pharmacol Sci 2024; 45:39-55. [PMID: 38072723 DOI: 10.1016/j.tips.2023.11.001] [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: 08/02/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.
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Affiliation(s)
- Balint Dudas
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France
| | - Maria A Miteva
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France.
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21
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Lv Q, Zhou F, Liu X, Zhi L. Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work? Bioorg Chem 2023; 141:106894. [PMID: 37776682 DOI: 10.1016/j.bioorg.2023.106894] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.
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Affiliation(s)
- Qi Lv
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China
| | - Feilong Zhou
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China
| | - Xinhua Liu
- School of Pharmacy, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230032, PR China.
| | - Liping Zhi
- School of Health Management, Anhui Medical University Hefei, 230032, PR China.
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Yamaguchi K, Isobe H, Shoji M, Kawakami T, Miyagawa K. The Nature of the Chemical Bonds of High-Valent Transition-Metal Oxo (M=O) and Peroxo (MOO) Compounds: A Historical Perspective of the Metal Oxyl-Radical Character by the Classical to Quantum Computations. Molecules 2023; 28:7119. [PMID: 37894598 PMCID: PMC10609222 DOI: 10.3390/molecules28207119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
This review article describes a historical perspective of elucidation of the nature of the chemical bonds of the high-valent transition metal oxo (M=O) and peroxo (M-O-O) compounds in chemistry and biology. The basic concepts and theoretical backgrounds of the broken-symmetry (BS) method are revisited to explain orbital symmetry conservation and orbital symmetry breaking for the theoretical characterization of four different mechanisms of chemical reactions. Beyond BS methods using the natural orbitals (UNO) of the BS solutions, such as UNO CI (CC), are also revisited for the elucidation of the scope and applicability of the BS methods. Several chemical indices have been derived as the conceptual bridges between the BS and beyond BS methods. The BS molecular orbital models have been employed to explain the metal oxyl-radical character of the M=O and M-O-O bonds, which respond to their radical reactivity. The isolobal and isospin analogy between carbonyl oxide R2C-O-O and metal peroxide LFe-O-O has been applied to understand and explain the chameleonic chemical reactivity of these compounds. The isolobal and isospin analogy among Fe=O, O=O, and O have also provided the triplet atomic oxygen (3O) model for non-heme Fe(IV)=O species with strong radical reactivity. The chameleonic reactivity of the compounds I (Cpd I) and II (Cpd II) is also explained by this analogy. The early proposals obtained by these theoretical models have been examined based on recent computational results by hybrid DFT (UHDFT), DLPNO CCSD(T0), CASPT2, and UNO CI (CC) methods and quantum computing (QC).
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Affiliation(s)
- Kizashi Yamaguchi
- SANKEN, Osaka University, Ibaraki 567-0047, Osaka, Japan
- Center for Quantum Information and Quantum Biology (QIQB), Osaka University, Toyonaka 560-0043, Osaka, Japan
| | - Hiroshi Isobe
- Research Institute for Interdisciplinary Science, Okayama University, Okayama 700-8530, Okayama, Japan;
| | - Mitsuo Shoji
- Center for Computational Sciences, University of Tsukuba, Tsukuba 305-8577, Ibaraki, Japan; (M.S.); (K.M.)
| | - Takashi Kawakami
- Department of Chemistry, Graduate School of Science, Osaka University, Toyonaka 560-0043, Osaka, Japan;
| | - Koichi Miyagawa
- Center for Computational Sciences, University of Tsukuba, Tsukuba 305-8577, Ibaraki, Japan; (M.S.); (K.M.)
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23
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Durgam L, Pagag J, Indra Neela Y, Guruprasad L. Mutational analyses, pharmacophore-based inhibitor design and in silico validation for Zika virus NS3-helicase. J Biomol Struct Dyn 2023; 42:9873-9891. [PMID: 37712848 DOI: 10.1080/07391102.2023.2252929] [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: 03/06/2023] [Accepted: 08/23/2023] [Indexed: 09/16/2023]
Abstract
Zika virus is responsible for causing Zika infections and was declared as a public health emergency of international concern in February 2016. The Zika virus NS3-helicase is a viable drug target for the design of inhibitors due to its essential role in the replication of viral genome. The viral RNA is unwound by the NS3-helicase in order to enable the reproduction of viral genome by the NS5 protein. Zika virus infections in humans are being reported for the last 15 years. We have therefore carried out amino acid mutational analyses of NS3-helicase. NS3-helicase has two crucial binding sites: the ATP and RNA binding sites. The cofactor-ATP based pharmacophore was generated for virtual screening of ZINC database using Pharmit server, that is followed by molecular docking and molecular dynamics simulations of potential hits as probable Zika virus NS3-helicase inhibitors at the cofactor binding site. The drug-like properties of the molecules were analysed and, DFT calculations were performed on the five best molecules to reveal their stability in solvent phase compared to gas phase, the HOMO and LUMO and electrostatic potential maps to analyze the electronic and geometric characteristics. These are significant findings towards the discovery of new inhibitors of Zika virus NS3-helicase, a promising drug target to treat the Zika virus infection.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Laxman Durgam
- School of Chemistry, University of Hyderabad, Hyderabad, India
| | - Jishu Pagag
- School of Chemistry, University of Hyderabad, Hyderabad, India
| | - Y Indra Neela
- School of Chemistry, University of Hyderabad, Hyderabad, India
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