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Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/1521-3838(200110)20:3<227::aid-qsar227>3.0.co;2-yopenalex-snapshot
Use of Support Vector Machine in Pattern Classification: Application to QSAR Studies
inferredcites →https://doi.org/10.1002/1521-3838(200208)21:3<249::aid-qsar249>3.0.co;2-sopenalex-snapshot
A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries
inferredcites →https://doi.org/10.1002/jps.20232openalex-snapshot
Quantitative Structure-Pharmacokinetic Relationships for Drug Distribution Properties by Using General Regression Neural Network
inferredcites →https://doi.org/10.1007/3-540-44673-7openalex-snapshot
Machine Learning and Its Applications: advanced lectures
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/b95439openalex-snapshot
Support Vector Machines: Theory and Applications
inferredcites →https://doi.org/10.1016/0005-2795(75)90109-9openalex-snapshot
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inferredcites →https://doi.org/10.1016/0040-4020(75)87084-0openalex-snapshot
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inferredcites →https://doi.org/10.1016/0169-7439(93)80079-wopenalex-snapshot
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inferredcites →https://doi.org/10.1016/0300-483x(95)03155-9openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0090-9556(24)15175-6openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-409x(02)00009-1openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-409x(02)00011-xopenalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-7439(02)00110-7openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-7439(96)00062-7openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-7439(98)00051-3openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0924-2031(99)00014-4openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0925-2312(03)00431-4openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0925-2312(03)00435-1openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0960-894x(01)00771-5openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1359-6446(01)01712-3openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1359-6446(97)01081-7openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1359-6446(97)01085-4openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci00015a005openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci00019a008openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci015504aopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci025639wopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci030283popenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034108kopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci034233wopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci049869hopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci049923uopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci950164copenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci980137xopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci980339topenalex-snapshot
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inferredcites →https://doi.org/10.1021/jm00196a017openalex-snapshot
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inferredcites →https://doi.org/10.1021/jm00196a018openalex-snapshot
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inferredcites →https://doi.org/10.1021/jm9700878openalex-snapshot
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inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
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inferredcites →https://doi.org/10.1023/a:1011911204383openalex-snapshot
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inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot
A tutorial on support vector regression
inferredcites →https://doi.org/10.1038/nrd1032openalex-snapshot
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inferredcites →https://doi.org/10.1080/1062936021000058782openalex-snapshot
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inferredcites →https://doi.org/10.1080/10629360290002280openalex-snapshot
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inferredcites →https://doi.org/10.1081/dmr-120001391openalex-snapshot
Structure–activity relationship for human cytochrome P450 substrates and inhibitors
inferredcites →https://doi.org/10.1081/dmr-120001392openalex-snapshot
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inferredcites →https://doi.org/10.1089/10665270260518317openalex-snapshot
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inferredcites →https://doi.org/10.1093/nar/gkg600openalex-snapshot
SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence
inferredcites →https://doi.org/10.1109/tai.2003openalex-snapshot
Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence
inferredcites →https://doi.org/10.1109/tai.2003.1250182openalex-snapshot
Feature selection for support vector machines by means of genetic algorithm
inferredcites →https://doi.org/10.1124/dmd.31.9.1077openalex-snapshot
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inferredcites →https://doi.org/10.1177/002029400103400803openalex-snapshot
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inferredcites →https://doi.org/10.1201/9781315273075openalex-snapshot
Randomization, Bootstrap and Monte Carlo Methods in Biology
inferredcites →https://doi.org/10.1248/cpb.40.127openalex-snapshot
Simple Method of Calculating Octanol/Water Partition Coefficient.
inferredcites →https://doi.org/10.1261/rna.5890304openalex-snapshot
Prediction of RNA-binding proteins from primary sequence by a support vector machine approach
inferredcites →https://doi.org/10.1289/ehp.5758openalex-snapshot
Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.
inferredcites →https://doi.org/10.1897/01-480openalex-snapshot
Quantitative structure-activity relationships for predicting metabolism and modeling cytochrome P450 enzyme activities
inferredcites →https://doi.org/10.2307/2533527openalex-snapshot
Randomization, Bootstrap and Monte Carlo Methods in Biology.
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9780470116449.ch6openalex-snapshot
Applications of Support Vector Machines in Chemistry
inferred← citeshttps://doi.org/10.1002/9780470921920.edm057openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9780470921920.edm139openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9781118838440.ch8openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527621286.ch4openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527645121.ch1openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527673261.ch13openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/bit.21214openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/ddr.20044openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jcc.21148openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jcc.21347openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jmr.2537openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jps.20985openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.200900086openalex-snapshot
Quantitative Prediction of Regioselectivity Toward Cytochrome P450/3A4 Using Machine Learning Approaches
inferred← citeshttps://doi.org/10.1002/minf.201000154openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201100052openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201100069openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201300009openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201400127openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201600153openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.202100113openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/pmic.200500938openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200630143openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200960021openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_135openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-27282-5_59openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-540-36266-1_10openalex-snapshot
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
inferred← citeshttps://doi.org/10.1007/978-94-007-6169-8_59-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00894-011-1105-5openalex-snapshot
CYP isoform specificity toward drug metabolism: analysis using common feature hypothesis
inferred← citeshttps://doi.org/10.1007/s10822-008-9225-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-010-9321-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-011-9468-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-020-00308-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12272-015-0705-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12539-021-00488-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b0-08-045044-x/00277-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-820472-6.00017-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.addr.2015.03.014openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.biomaterials.2013.10.069openalex-snapshot
Discovery of antibiotics-derived polymers for gene delivery using combinatorial synthesis and cheminformatics modeling
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.03.065openalex-snapshot
Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists
inferred← citeshttps://doi.org/10.1016/j.bmc.2012.01.049openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.chemolab.2012.05.012openalex-snapshot
In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2010.09.004openalex-snapshot
The computational model to predict accurately inhibitory activity for inhibitors towardsCYP3A4
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2013.01.015openalex-snapshot
In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
inferred← citeshttps://doi.org/10.1016/j.ddtec.2012.09.007openalex-snapshot
Modelling cytochromes P450 binding modes to predict P450 inhibition, metabolic stability and isoform selectivity
inferred← citeshttps://doi.org/10.1016/j.dmpk.2019.11.006openalex-snapshot
Computational prediction of cytochrome P450 inhibition and induction
inferred← citeshttps://doi.org/10.1016/j.envpol.2023.122358openalex-snapshot
Advances and applications of machine learning and deep learning in environmental ecology and health
inferred← citeshttps://doi.org/10.1016/j.jmgm.2005.10.004openalex-snapshot
Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2006.01.007openalex-snapshot
Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.03.003openalex-snapshot
Prediction of factor Xa inhibitors by machine learning methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.12.002openalex-snapshot
A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2008.08.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jmgm.2009.08.001openalex-snapshot
Prediction of novel and selective TNF-alpha converting enzyme (TACE) inhibitors and characterization of correlative molecular descriptors by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.05.006openalex-snapshot
Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach
inferred← citeshttps://doi.org/10.1016/j.jtbi.2014.04.021openalex-snapshot
Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM
inferred← citeshttps://doi.org/10.1016/j.ygeno.2018.12.007openalex-snapshot
Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure
inferred← citeshttps://doi.org/10.1016/s1574-1400(06)02008-1openalex-snapshot
Chapter 8 Machine Learning in Computational Chemistry
inferred← citeshttps://doi.org/10.1016/s1574-1400(07)03005-8openalex-snapshot
Chapter 5 Linear Quantitative Structure–Activity Relationships for the Interaction of Small Molecules with Human Cytochrome P450 Isoenzymes
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00065openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00469openalex-snapshot
Simulation of the Bottleneck Controlling Access into a Rieske Active Site: Predicting Substrates of Naphthalene 1,2-Dioxygenase
inferred← citeshttps://doi.org/10.1021/acsbiomaterials.5b00045openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acsomega.5c08756openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acssynbio.5c00779openalex-snapshot
Machine Learning-Assisted Discovery of Cytochromes P450 Putatively Involved in the Biosynthesis of Amaryllidaceae Alkaloids in Crinum asiaticum L.
inferred← citeshttps://doi.org/10.1021/ci200028nopenalex-snapshot
Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers
inferred← citeshttps://doi.org/10.1021/ci2000488openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci200317sopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci200542mopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci300009zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci3001875openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci400518gopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci5000467openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci6002619openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci7002076openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci800001mopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci800022eopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci8000953openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900011gopenalex-snapshot
EaMEAD: Activation Energy Prediction of Cytochrome P450 Mediated Metabolism with Effective Atomic Descriptors
inferred← citeshttps://doi.org/10.1021/ci900074topenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900135uopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900247topenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci900299aopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/jm0601553openalex-snapshot
Line-Walking Method for Predicting the Inhibition of P450 Drug Metabolism
inferred← citeshttps://doi.org/10.1021/jm7009793openalex-snapshot
Prediction of Metabolism by Cytochrome P450 2C9: Alignment and Docking Studies of a Validated Database of Substrates
inferred← citeshttps://doi.org/10.1021/jm800504qopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/mp900217xopenalex-snapshot
Classification of Cytochrome P450 Activities Using Machine Learning Methods
inferred← citeshttps://doi.org/10.1038/s41597-025-05753-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1039/c8ra06311gopenalex-snapshot
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inferred← citeshttps://doi.org/10.1039/d3nj02471gopenalex-snapshot
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inferred← citeshttps://doi.org/10.1063/1674-0068/29/cjcp1603039openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/03602532.2021.1874406openalex-snapshot
Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review
inferred← citeshttps://doi.org/10.1080/10629360600934168openalex-snapshot
Comparison of methods for chemical-compound affinity prediction
inferred← citeshttps://doi.org/10.1080/10629360902949195openalex-snapshot
QSAR models for P450 (2D6) substrate activity
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1040453openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/17425255.2017.1316449openalex-snapshot
Characterisation of data resources forin silicomodelling: benchmark datasets for ADME properties
inferred← citeshttps://doi.org/10.1080/17460441.2025.2491669openalex-snapshot
Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design
inferred← citeshttps://doi.org/10.1101/2022.09.27.509731openalex-snapshot
An analysis of laboratory variability and thresholds for human in vitro ADME/PK methods
inferred← citeshttps://doi.org/10.1101/2022.12.13.520246openalex-snapshot
Comparing in silico and in vitro methods for classification of BCS II and CYP3A4 and MDR-1 substrate specificity
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2011.01137.xopenalex-snapshot
Optimizing QSAR Models for Predicting Ligand Binding to the Drug‐Metabolizing Cytochrome P450 Isoenzyme CYP2D6
inferred← citeshttps://doi.org/10.1124/dmd.108.023507openalex-snapshot
Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques
inferred← citeshttps://doi.org/10.1124/mol.106.027623openalex-snapshot
In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approache
inferred← citeshttps://doi.org/10.1128/mbio.03705-21openalex-snapshot
The FDA-Approved Drug Cobicistat Synergizes with Remdesivir To Inhibit SARS-CoV-2 Replication In Vitro and Decreases Viral Titers and Disease Progression in Syrian Hamsters
inferred← citeshttps://doi.org/10.1134/s1990750810010105openalex-snapshot
Computer-based substrate specificity prediction for cytochrome P450
inferred← citeshttps://doi.org/10.1142/s0219720011005434openalex-snapshot
TRANSFER LEARNING FOR CYTOCHROME P450 ISOZYME SELECTIVITY PREDICTION
inferred← citeshttps://doi.org/10.1145/2339530.2339614openalex-snapshot
A structural cluster kernel for learning on graphs
inferred← citeshttps://doi.org/10.11606/d.75.2011.tde-17042012-103806openalex-snapshot
Sobre os estudos metabólicos de fármacos empregando-se atividade enzimática de CYP450 visando-se estabelecer correlações entre estrutura e atividade
inferred← citeshttps://doi.org/10.11606/d.75.2012.tde-17042012-103806openalex-snapshot
Sobre os estudos metabólicos de fármacos empregando-se atividade enzimática de CYP450 visando-se estabelecer correlações entre estrutura e atividade
inferred← citeshttps://doi.org/10.11606/t.76.2011.tde-19102011-141448openalex-snapshot
Modelagem >i /i< de propriedades farmacocinéticas para a avaliação de candidatos a novos fármacos
inferred← citeshttps://doi.org/10.1186/1471-2210-10-8openalex-snapshot
Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
inferred← citeshttps://doi.org/10.1186/1752-0509-1-s1-p23openalex-snapshot
In silico prediction system of CYP450-mediated metabolism profile
inferred← citeshttps://doi.org/10.1186/1752-153x-6-139openalex-snapshot
Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries
inferred← citeshttps://doi.org/10.1186/1758-2946-3-22openalex-snapshot
Predicting a small molecule-kinase interaction map: A machine learning approach
inferred← citeshttps://doi.org/10.1186/s13321-022-00602-xopenalex-snapshot
Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.12732/ijpam.v87i6.2openalex-snapshot
SVM TRADE-OFF BETWEEN MAXIMIZE THE MARGIN AND MINIMIZE THE VARIABLES USED FOR REGRESSION
inferred← citeshttps://doi.org/10.1371/journal.pone.0129974openalex-snapshot
Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling
inferred← citeshttps://doi.org/10.1517/17425250902753261openalex-snapshot
The use of machine learning and nonlinear statistical tools for ADME prediction
inferred← citeshttps://doi.org/10.1517/17425255.2.4.545openalex-snapshot
Structure-based methods for the prediction of drug metabolism
inferred← citeshttps://doi.org/10.1517/17425255.2011.611501openalex-snapshot
Computational modeling of P450s for toxicity prediction
inferred← citeshttps://doi.org/10.18097/pbmc20105601090openalex-snapshot
Computer-based substrate specifity prediction for cytochrome P450
inferred← citeshttps://doi.org/10.21203/rs.2.9738/v1openalex-snapshot
Computational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method
inferred← citeshttps://doi.org/10.2165/11316630-000000000-00000openalex-snapshot
Predicting the Clinical Relevance of Drug Interactions From Pre-Approval Studies
inferred← citeshttps://doi.org/10.2174/1381612825666190903155935openalex-snapshot
In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery
inferred← citeshttps://doi.org/10.2174/138920011795495259openalex-snapshot
Advances in Human Cytochrome P450 and Personalized Medicine
inferred← citeshttps://doi.org/10.2174/1389200219666181019094526openalex-snapshot
Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates
inferred← citeshttps://doi.org/10.2174/1568026619666190123160406openalex-snapshot
Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes
inferred← citeshttps://doi.org/10.2174/187231308785739747openalex-snapshot
Advancements in Predictive In Silico Models for ADME
inferred← citeshttps://doi.org/10.2174/978160805379711201010119openalex-snapshot
Bridging Chemical and Biological Space: QSAR Probing Using 3D Molecular Descriptors
inferred← citeshttps://doi.org/10.2751/jcac.11.19openalex-snapshot
Bayesian Classification of Cytochrome P450 3A4 Substrates/Non-substrates and Color Mapping for Chemical Interpretation
inferred← citeshttps://doi.org/10.2788/94537openalex-snapshot
Review of QSAR Models and Software Tools for predicting Biokinetic Properties
inferred← citeshttps://doi.org/10.2991/meita-15.2015.119openalex-snapshot
A combination of support vector regression, structure-based pharmacophore and virtual screening for cytochrome P450 2C9 inhibitors from Chinese herbs
inferred← citeshttps://doi.org/10.3390/computation7020026openalex-snapshot
Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models
inferred← citeshttps://doi.org/10.3390/ijms17060914openalex-snapshot
In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9
inferred← citeshttps://doi.org/10.3390/life13010127openalex-snapshot
3D-QSAR Studies, Molecular Docking, Molecular Dynamic Simulation, and ADMET Proprieties of Novel Pteridinone Derivatives as PLK1 Inhibitors for the Treatment of Prostate Cancer
inferred← citeshttps://doi.org/10.3390/molecules17033383openalex-snapshot
Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes
inferred← citeshttps://doi.org/10.3390/molecules17033407openalex-snapshot
Modeling Chemical Interaction Profiles: II. Molecular Docking, Spectral Data-Activity Relationship, and Structure-Activity Relationship Models for Potent and Weak Inhibitors of Cytochrome P450 CYP3A4 Isozyme
inferred← citeshttps://doi.org/10.3390/molecules17044560openalex-snapshot
A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity
inferred← citeshttps://doi.org/10.3390/molecules24193435openalex-snapshot
Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti-Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin
inferred← citeshttps://doi.org/10.4051/ibc.2012.4.1.0002openalex-snapshot
Classification of HDAC8 Inhibitors and Non-Inhibitors Using Support Vector Machines
inferred← citeshttps://doi.org/10.4137/bbi.s315openalex-snapshot
Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines
inferred← citeshttps://doi.org/10.4155/fmc-2017-0303openalex-snapshot
The Current Limits in Virtual Screening and Property Prediction
inferred← citeshttps://doi.org/10.4314/tjpr.v12i6.7openalex-snapshot
Multivariate Modeling of Cytochrome P450 Enzymes for 4- Aminoquinoline Antimalarial Analogues using Genetic- Algorithms Multiple Linear Regression
inferred← citeshttps://doi.org/10.5772/14102openalex-snapshot
Atom Coloring for Chemical Interpretation and De Novo Design for Molecular Design
inferred← citeshttps://doi.org/10.6084/m9.figshare.19607285openalex-snapshot
Additional file 1 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.6084/m9.figshare.19607288openalex-snapshot
Additional file 2 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.6084/m9.figshare.19607291openalex-snapshot
Additional file 3 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.6084/m9.figshare.19607294openalex-snapshot
Additional file 4 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.6084/m9.figshare.19607297openalex-snapshot
Additional file 5 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.6084/m9.figshare.19607300openalex-snapshot
Additional file 6 of Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes
inferred← citeshttps://doi.org/10.7939/r3g44hw2xopenalex-snapshot
Learning to predict the sites of metabolism and metabolic endpoints
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