258 related articles for article (PubMed ID: 19239395)
1. The use of machine learning and nonlinear statistical tools for ADME prediction.
Sakiyama Y
Expert Opin Drug Metab Toxicol; 2009 Feb; 5(2):149-69. PubMed ID: 19239395
[TBL] [Abstract][Full Text] [Related]
2. Gaussian processes: a method for automatic QSAR modeling of ADME properties.
Obrezanova O; Csanyi G; Gola JM; Segall MD
J Chem Inf Model; 2007; 47(5):1847-57. PubMed ID: 17602549
[TBL] [Abstract][Full Text] [Related]
3. Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.
Tao L; Zhang P; Qin C; Chen SY; Zhang C; Chen Z; Zhu F; Yang SY; Wei YQ; Chen YZ
Adv Drug Deliv Rev; 2015 Jun; 86():83-100. PubMed ID: 26037068
[TBL] [Abstract][Full Text] [Related]
4. Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
Schroeter TS; Schwaighofer A; Mika S; Ter Laak A; Suelzle D; Ganzer U; Heinrich N; Müller KR
J Comput Aided Mol Des; 2007 Sep; 21(9):485-98. PubMed ID: 17632688
[TBL] [Abstract][Full Text] [Related]
5. Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery.
Khakar PS
Curr Top Med Chem; 2010; 10(1):116-26. PubMed ID: 19929825
[TBL] [Abstract][Full Text] [Related]
6. Applying machine learning techniques for ADME-Tox prediction: a review.
Maltarollo VG; Gertrudes JC; Oliveira PR; Honorio KM
Expert Opin Drug Metab Toxicol; 2015 Feb; 11(2):259-71. PubMed ID: 25440524
[TBL] [Abstract][Full Text] [Related]
7. Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.
Kumar R; Sharma A; Siddiqui MH; Tiwari RK
Curr Drug Discov Technol; 2017; 14(4):244-254. PubMed ID: 28382857
[TBL] [Abstract][Full Text] [Related]
8. truPK -- human pharmacokinetic models for quantitative ADME prediction.
Subramanian K
Expert Opin Drug Metab Toxicol; 2005 Oct; 1(3):555-64. PubMed ID: 16863461
[TBL] [Abstract][Full Text] [Related]
9. Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery.
Bhatia AS; Saggi MK; Kais S
J Chem Inf Model; 2023 Nov; 63(21):6476-6486. PubMed ID: 37603536
[TBL] [Abstract][Full Text] [Related]
10. Recent developments of in silico predictions of intestinal absorption and oral bioavailability.
Hou T; Li Y; Zhang W; Wang J
Comb Chem High Throughput Screen; 2009 Jun; 12(5):497-506. PubMed ID: 19519329
[TBL] [Abstract][Full Text] [Related]
11. Predicting human liver microsomal stability with machine learning techniques.
Sakiyama Y; Yuki H; Moriya T; Hattori K; Suzuki M; Shimada K; Honma T
J Mol Graph Model; 2008 Feb; 26(6):907-15. PubMed ID: 17683964
[TBL] [Abstract][Full Text] [Related]
12. Structure-ADME relationship: still a long way to go?
Hou T; Wang J
Expert Opin Drug Metab Toxicol; 2008 Jun; 4(6):759-70. PubMed ID: 18611116
[TBL] [Abstract][Full Text] [Related]
13. Machine learning techniques for in silico modeling of drug metabolism.
Fox T; Kriegl JM
Curr Top Med Chem; 2006; 6(15):1579-91. PubMed ID: 16918470
[TBL] [Abstract][Full Text] [Related]
14. Artificial intelligence in drug design.
Zhong F; Xing J; Li X; Liu X; Fu Z; Xiong Z; Lu D; Wu X; Zhao J; Tan X; Li F; Luo X; Li Z; Chen K; Zheng M; Jiang H
Sci China Life Sci; 2018 Oct; 61(10):1191-1204. PubMed ID: 30054833
[TBL] [Abstract][Full Text] [Related]
15. Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties.
Kosugi Y; Hosea N
Mol Pharm; 2021 Mar; 18(3):1071-1079. PubMed ID: 33512165
[TBL] [Abstract][Full Text] [Related]
16. Improving early drug discovery through ADME modelling: an overview.
Wishart DS
Drugs R D; 2007; 8(6):349-62. PubMed ID: 17963426
[TBL] [Abstract][Full Text] [Related]
17. Virtual screening for cytochromes p450: successes of machine learning filters.
Burton J; Ijjaali I; Petitet F; Michel A; Vercauteren DP
Comb Chem High Throughput Screen; 2009 May; 12(4):369-82. PubMed ID: 19442071
[TBL] [Abstract][Full Text] [Related]
18. Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.
Di Lascio E; Gerebtzoff G; Rodríguez-Pérez R
Mol Pharm; 2023 Mar; 20(3):1758-1767. PubMed ID: 36745394
[TBL] [Abstract][Full Text] [Related]
19. Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels.
Su R; Li Y; Zink D; Loo LH
BMC Bioinformatics; 2014; 15 Suppl 16(Suppl 16):S16. PubMed ID: 25521947
[TBL] [Abstract][Full Text] [Related]
20. Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.
Wenzel J; Matter H; Schmidt F
J Chem Inf Model; 2019 Mar; 59(3):1253-1268. PubMed ID: 30615828
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
