Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives
暂无分享,去创建一个
[1] Connor W. Coley,et al. Artificial intelligence foundation for therapeutic science , 2022, Nature Chemical Biology.
[2] Vigneshwar Subramanian,et al. Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images , 2022, Journal of Computer-Aided Molecular Design.
[3] Hua Wu,et al. HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer , 2022, Bioinform..
[4] Jianping Lin,et al. Interpretable-ADMET: a web service for ADMET prediction and optimization based on deep neural representation , 2022, Bioinform..
[5] T. Aittokallio. What are the current challenges for machine learning in drug discovery and repurposing? , 2022, Expert opinion on drug discovery.
[6] X. Chu,et al. Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development , 2022, Acta pharmaceutica Sinica. B.
[7] Y. Terelius,et al. Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work , 2022, Drug Metabolism and Disposition.
[8] Bo Wang,et al. HobPre: accurate prediction of human oral bioavailability for small molecules , 2022, Journal of Cheminformatics.
[9] M. Loriot,et al. Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 , 2022, PLoS Comput. Biol..
[10] R. Greiner,et al. HMDB 5.0: the Human Metabolome Database for 2022 , 2021, Nucleic Acids Res..
[11] Y. Terelius,et al. Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work , 2021, Drug Metabolism and Disposition.
[12] Graham F. Smith. Artificial Intelligence in Drug Safety and Metabolism. , 2021, Methods in molecular biology.
[13] Yang Liu,et al. Classification and prediction model of compound pharmacokinetic properties based on ensemble learning method , 2021, ISAIMS.
[14] L. Kavraki,et al. Machine learning models in the prediction of drug metabolism: challenges and future perspectives , 2021, Expert opinion on drug metabolism & toxicology.
[15] Binh P. Nguyen,et al. iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding , 2021, J. Chem. Inf. Model..
[16] Vishwesh Venkatraman,et al. FP-ADMET: a compendium of fingerprint-based ADMET prediction models , 2021, Journal of Cheminformatics.
[17] Y. Uesawa,et al. Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning , 2021, ACS omega.
[18] J. Kirchmair,et al. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. , 2021, Bioorganic & medicinal chemistry.
[19] R. Segurado,et al. Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study , 2021, Journal of proteome research.
[20] Weihua Li,et al. In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods. , 2021, Chemical research in toxicology.
[21] David S. Wishart,et al. CyProduct: A Software Tool for Accurately Predicting the Byproducts of Human Cytochrome P450 Metabolism , 2021, J. Chem. Inf. Model..
[22] D. Thompson,et al. Drug Metabolism in Drug Discovery and Preclinical Development , 2021, Drug Metabolism [Working Title].
[23] Alya A Arabi,et al. Artificial intelligence in drug design: algorithms, applications, challenges and ethics , 2021, Future Drug Discovery.
[24] Aiping Lu,et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties , 2021, Nucleic Acids Res..
[25] Tomoki Suzuki,et al. Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model. , 2021, Drug metabolism and pharmacokinetics.
[26] O. Silakari,et al. Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors of CYP1B1 , 2021, Journal of biomolecular structure & dynamics.
[27] Yurii S Moroz,et al. ZINC20 - A Free Ultralarge-Scale Chemical Database for Ligand Discovery , 2020, J. Chem. Inf. Model..
[28] J. Kirchmair,et al. GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics , 2020, Chemical research in toxicology.
[29] Bo-Han Su,et al. Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development , 2020, Briefings Bioinform..
[30] Gisbert Schneider,et al. Drug discovery with explainable artificial intelligence , 2020, Nature Machine Intelligence.
[31] Y. Kosugi,et al. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-up Approach Using In Vitro Assay. , 2020, Molecular pharmaceutics.
[32] Hyun Kil Shin,et al. PreMetabo: An in silico phase I and II drug metabolism prediction platform. , 2020, Drug metabolism and pharmacokinetics.
[33] György M. Keserü,et al. Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling , 2020, Journal of Computer-Aided Molecular Design.
[34] R. Preissner,et al. SuperCYPsPred—a web server for the prediction of cytochrome activity , 2020, Nucleic Acids Res..
[35] Weihua Li,et al. In silico prediction of human renal clearance of compounds using quantitative structure-pharmacokinetic relationship models. , 2020, Chemical research in toxicology.
[36] Gary Siuzdak,et al. METLIN: A Tandem Mass Spectral Library of Standards. , 2020, Methods in molecular biology.
[37] Shiva Kumar,et al. Multi-omics Data Integration, Interpretation, and Its Application , 2020, Bioinformatics and biology insights.
[38] Jinyun Dong,et al. Synthesis and structure-activity relationship studies of α-naphthoflavone derivatives as CYP1B1 inhibitors. , 2019, European journal of medicinal chemistry.
[39] K. Mizuguchi,et al. Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor , 2019, Scientific Reports.
[40] Hu Mei,et al. Molecular image-based convolutional neural network for the prediction of ADMET properties , 2019, Chemometrics and Intelligent Laboratory Systems.
[41] Bernhard O. Palsson,et al. BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree , 2019, Nucleic Acids Res..
[42] Claire O'Donovan,et al. MetaboLights: a resource evolving in response to the needs of its scientific community , 2019, Nucleic Acids Res..
[43] Chao Shen,et al. ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches , 2019, J. Chem. Inf. Model..
[44] Z. Zuo,et al. Current trends in drug metabolism and pharmacokinetics , 2019, Acta pharmaceutica Sinica. B.
[45] Dong-Qing Wei,et al. Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method , 2019, J. Chem. Inf. Model..
[46] Yan Yang,et al. In Silico Prediction of Human Intravenous Pharmacokinetic Parameters with Improved Accuracy , 2019, J. Chem. Inf. Model..
[47] Daniel Svozil,et al. NERDD: a web portal providing access to in silico tools for drug discovery , 2019, Bioinform..
[48] Flemming Steen Jørgensen,et al. SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server , 2019, Bioinform..
[49] Daniel Svozil,et al. FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes , 2019, J. Chem. Inf. Model..
[50] G. Schneider,et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. , 2019, Chemical reviews.
[51] Lei Jia,et al. Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction , 2018, International journal of molecular sciences.
[52] Daniel Svozil,et al. GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism , 2019, Front. Chem..
[53] Weiqi Wang,et al. Computational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method , 2019 .
[54] L. Wienkers,et al. Handbook of Drug Metabolism, Third Edition , 2019 .
[55] Ren Jun,et al. In silico approaches and tools for the prediction of drug metabolism and fate: A review , 2019, Comput. Biol. Medicine.
[56] Robert P Sheridan,et al. Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It? , 2019, J. Chem. Inf. Model..
[57] Andreas Bender,et al. Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database. , 2019, ACS medicinal chemistry letters.
[58] T. Itoh,et al. Design and synthesis of selective CYP1B1 inhibitor via dearomatization of α-naphthoflavone. , 2019, Bioorganic & medicinal chemistry.
[59] K. Friedemann Schmidt,et al. Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets , 2019, J. Chem. Inf. Model..
[60] David S. Wishart,et al. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification , 2019, Journal of Cheminformatics.
[61] Hongbin Yang,et al. Computational Prediction of Site of Metabolism for UGT-Catalyzed Reactions , 2018, J. Chem. Inf. Model..
[62] Jinyun Dong,et al. Design, Synthesis, and Biological Evaluation of Cytochrome P450 1B1 Targeted Molecular Imaging Probes for Colorectal Tumor Detection. , 2018, Journal of medicinal chemistry.
[63] Andrew R. Leach,et al. ChEMBL: towards direct deposition of bioassay data , 2018, Nucleic Acids Res..
[64] Evan Bolton,et al. PubChem 2019 update: improved access to chemical data , 2018, Nucleic Acids Res..
[65] T. Maurer,et al. Clearance in Drug Design. , 2018, Journal of medicinal chemistry.
[66] Kenji Mizuguchi,et al. Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance , 2018, Molecular informatics.
[67] Jie Li,et al. admetSAR 2.0: web‐service for prediction and optimization of chemical ADMET properties , 2018, Bioinform..
[68] Franco Lombardo,et al. Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds , 2018, Drug Metabolism and Disposition.
[69] Volkan Atalay,et al. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases , 2018, Briefings Bioinform..
[70] Jianfeng Pei,et al. Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network. , 2018, Molecular pharmaceutics.
[71] David S. Wishart,et al. CypReact: A Software Tool for in Silico Reactant Prediction for Human Cytochrome P450 Enzymes , 2018, J. Chem. Inf. Model..
[72] Wei Tang,et al. Drug metabolism in drug discovery and development , 2018, Acta pharmaceutica Sinica. B.
[73] Sabina Podlewska,et al. MetStabOn—Online Platform for Metabolic Stability Predictions , 2018, International journal of molecular sciences.
[74] B. Testa,et al. MetaQSAR: An Integrated Database Engine to Manage and Analyze Metabolic Data. , 2017, Journal of medicinal chemistry.
[75] David S. Wishart,et al. DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..
[76] T. Maurer,et al. Relevance of Half-Life in Drug Design. , 2017, Journal of medicinal chemistry.
[77] S. Gad,et al. Nonclinical Drug Administration: Formulations, Routes and Regimens for Solving Drug Delivery Problems in Animal Model Systems , 2017 .
[78] Daniel Svozil,et al. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity , 2017, J. Chem. Inf. Model..
[79] Olivier Michielin,et al. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules , 2017, Scientific Reports.
[80] Andrew A Somogyi,et al. Advances in drug metabolism and pharmacogenetics research in Australia , 2017, Pharmacological research.
[81] Minoru Kanehisa,et al. KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..
[82] Aleksandra Galetin,et al. Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance , 2016, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.
[83] P. Neuvonen,et al. Role of Cytochrome P450 2C8 in Drug Metabolism and Interactions , 2016, Pharmacological Reviews.
[84] Zhiheng Xu,et al. OpenFDA: an innovative platform providing access to a wealth of FDA’s publicly available data , 2015, J. Am. Medical Informatics Assoc..
[85] Bo-Han Su,et al. Rule-Based Prediction Models of Cytochrome P450 Inhibition , 2015, J. Chem. Inf. Model..
[86] Vladimir Poroikov,et al. SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds , 2015, Bioinform..
[87] Bo-Han Su,et al. CypRules: a rule-based P450 inhibition prediction server , 2015, Bioinform..
[88] Dieter Lang,et al. Predicting drug metabolism: experiment and/or computation? , 2015, Nature Reviews Drug Discovery.
[89] Franco Lombardo,et al. Clearance mechanism assignment and total clearance prediction in human based upon in silico models. , 2014, Journal of medicinal chemistry.
[90] Xiaomin Luo,et al. In silico site of metabolism prediction for human UGT-catalyzed reactions , 2014, Bioinform..
[91] Sanjay Joshua Swamidass,et al. XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks , 2013, J. Chem. Inf. Model..
[92] Robert C. Glen,et al. FAst MEtabolizer (FAME): A Rapid and Accurate Predictor of Sites of Metabolism in Multiple Species by Endogenous Enzymes , 2013, J. Chem. Inf. Model..
[93] Ulf Norinder,et al. In silico categorization of in vivo intrinsic clearance using machine learning. , 2013, Molecular pharmaceutics.
[94] Alessandro Pedretti,et al. Reactions and enzymes in the metabolism of drugs and other xenobiotics. , 2012, Drug discovery today.
[95] Igor V. Tetko,et al. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Comput. Aided Mol. Des..
[96] Rebecca Denton,et al. A rapid computational filter for predicting the rate of human renal clearance. , 2010, Journal of molecular graphics & modelling.
[97] Suzanne M. Paley,et al. Beyond the genome (BTG) is a (PGDB) pathway genome database: HumanCyc , 2010, Genome Biology.
[98] Antony J. Williams,et al. ChemSpider:: An Online Chemical Information Resource , 2010 .
[99] David E. Gloriam,et al. SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism. , 2010, ACS medicinal chemistry letters.
[100] R Scott Obach,et al. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. , 2010, Journal of medicinal chemistry.
[101] Michael Schroeder,et al. SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions , 2009, Nucleic Acids Res..
[102] Jürgen Pleiss,et al. The Cytochrome P450 Engineering Database: integration of biochemical properties , 2009, BMC Biochemistry.
[103] Ruili Huang,et al. Comprehensive Characterization of Cytochrome P450 Isozyme Selectivity across Chemical Libraries , 2009, Nature Biotechnology.
[104] R. Obach,et al. Physicochemical determinants of human renal clearance. , 2009, Journal of medicinal chemistry.
[105] Yong Wang,et al. Site of metabolism prediction for six biotransformations mediated by cytochromes P450 , 2009, Bioinform..
[106] L. Wienkers,et al. Handbook of Drug Metabolism , 2008 .
[107] Lars Ridder,et al. SyGMa: Combining Expert Knowledge and Empirical Scoring in the Prediction of Metabolites , 2008, ChemMedChem.
[108] Jürgen Pleiss,et al. The Cytochrome P450 Engineering Database: a navigation and prediction tool for the cytochrome P450 protein family , 2007, Bioinform..
[109] Kiyoko F. Aoki-Kinoshita,et al. From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..
[110] R. Obach,et al. Examination of 209 Drugs for Inhibition of Cytochrome P450 2C8 , 2005, Journal of clinical pharmacology.
[111] J. Miners,et al. Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDP-glucuronosyltransferases. , 2004, Journal of molecular graphics & modelling.
[112] Tingjun Hou,et al. ADME evaluation in drug discovery , 2002, Journal of molecular modeling.
[113] Gregory Bock,et al. 'In silico' simulation of biological processes , 2002 .
[114] Stefan Schiesser,et al. Advances in the design of new types of inhaled medicines. , 2022, Progress in medicinal chemistry.
[115] OUP accepted manuscript , 2021, Nucleic Acids Research.
[116] Thomas R Larson,et al. Drug Excretion , 2021, Reference Module in Biomedical Sciences.
[117] Rodziah Atan,et al. A Review on Integration of Scientific Experimental Data Through Metadata , 2019, Recent Trends and Advances in Wireless and IoT-enabled Networks.
[118] A. Talevi,et al. ADME Processes in Pharmaceutical Sciences , 2018, Springer International Publishing.
[119] F. Guengerich. Cytochrome p450 and chemical toxicology. , 2008, Chemical research in toxicology.
[120] H. Matter,et al. In-Silico ADME Modeling , 2006 .
[121] Susumu Goto,et al. The KEGG databases at GenomeNet , 2002, Nucleic Acids Res..