230 related articles for article (PubMed ID: 33991751)
1. Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model.
Sasahara K; Shibata M; Sasabe H; Suzuki T; Takeuchi K; Umehara K; Kashiyama E
Drug Metab Pharmacokinet; 2021 Aug; 39():100395. PubMed ID: 33991751
[TBL] [Abstract][Full Text] [Related]
2. Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.
Sasahara K; Shibata M; Sasabe H; Suzuki T; Takeuchi K; Umehara K; Kashiyama E
Drug Metab Pharmacokinet; 2021 Aug; 39():100401. PubMed ID: 34089983
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery.
Tyzack JD; Kirchmair J
Chem Biol Drug Des; 2019 Apr; 93(4):377-386. PubMed ID: 30471192
[TBL] [Abstract][Full Text] [Related]
5. Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.
Paliwal A; Jain S; Kumar S; Wal P; Khandai M; Khandige PS; Sadananda V; Anwer MK; Gulati M; Behl T; Srivastava S
Expert Opin Drug Metab Toxicol; 2024 Apr; 20(4):181-195. PubMed ID: 38480460
[TBL] [Abstract][Full Text] [Related]
6. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.
Kosugi Y; Hosea N
Mol Pharm; 2020 Jul; 17(7):2299-2309. PubMed ID: 32478525
[TBL] [Abstract][Full Text] [Related]
7. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.
Liu R; Schyman P; Wallqvist A
J Chem Inf Model; 2015 Aug; 55(8):1566-75. PubMed ID: 26170251
[TBL] [Abstract][Full Text] [Related]
8. Prediction of Oral Bioavailability in Rats: Transferring Insights from in Vitro Correlations to (Deep) Machine Learning Models Using in Silico Model Outputs and Chemical Structure Parameters.
Schneckener S; Grimbs S; Hey J; Menz S; Osmers M; Schaper S; Hillisch A; Göller AH
J Chem Inf Model; 2019 Nov; 59(11):4893-4905. PubMed ID: 31714067
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. The current limits in virtual screening and property prediction.
Hutter MC
Future Med Chem; 2018 Jul; 10(13):1623-1635. PubMed ID: 29953247
[TBL] [Abstract][Full Text] [Related]
11. Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.
Perryman AL; Stratton TP; Ekins S; Freundlich JS
Pharm Res; 2016 Feb; 33(2):433-49. PubMed ID: 26415647
[TBL] [Abstract][Full Text] [Related]
12. Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.
Fagerholm U; Hellberg S; Spjuth O
Molecules; 2021 Apr; 26(9):. PubMed ID: 33925103
[TBL] [Abstract][Full Text] [Related]
13. Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.
Watanabe R; Kawata T; Ueda S; Shinbo T; Higashimori M; Natsume-Kitatani Y; Mizuguchi K
Mol Pharm; 2023 Jan; 20(1):419-426. PubMed ID: 36538346
[TBL] [Abstract][Full Text] [Related]
14. Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.
Obrezanova O; Martinsson A; Whitehead T; Mahmoud S; Bender A; Miljković F; Grabowski P; Irwin B; Oprisiu I; Conduit G; Segall M; Smith GF; Williamson B; Winiwarter S; Greene N
Mol Pharm; 2022 May; 19(5):1488-1504. PubMed ID: 35412314
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance.
Ring BJ; Chien JY; Adkison KK; Jones HM; Rowland M; Jones RD; Yates JW; Ku MS; Gibson CR; He H; Vuppugalla R; Marathe P; Fischer V; Dutta S; Sinha VK; Björnsson T; Lavé T; Poulin P
J Pharm Sci; 2011 Oct; 100(10):4090-110. PubMed ID: 21541938
[TBL] [Abstract][Full Text] [Related]
17. Integration of in silico and in vitro tools for scaffold optimization during drug discovery: predicting P-glycoprotein efflux.
Desai PV; Sawada GA; Watson IA; Raub TJ
Mol Pharm; 2013 Apr; 10(4):1249-61. PubMed ID: 23363443
[TBL] [Abstract][Full Text] [Related]
18. Modeling of human cytochrome p450-mediated drug metabolism using unsupervised machine learning approach.
Korolev D; Balakin KV; Nikolsky Y; Kirillov E; Ivanenkov YA; Savchuk NP; Ivashchenko AA; Nikolskaya T
J Med Chem; 2003 Aug; 46(17):3631-43. PubMed ID: 12904067
[TBL] [Abstract][Full Text] [Related]
19. In Silico Prediction of Compounds Binding to Human Plasma Proteins by QSAR Models.
Sun L; Yang H; Li J; Wang T; Li W; Liu G; Tang Y
ChemMedChem; 2018 Mar; 13(6):572-581. PubMed ID: 29057587
[TBL] [Abstract][Full Text] [Related]
20. In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.
Hu J; Cai Y; Li W; Liu G; Tang Y
Mol Inform; 2020 Aug; 39(8):e1900178. PubMed ID: 32162831
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]