Detalhe da pesquisa
1.
Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.
J Chem Inf Model
; 64(2): 348-358, 2024 Jan 22.
Artigo
em Inglês
| MEDLINE | ID: mdl-38170877
2.
Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.
Mol Pharm
; 19(5): 1488-1504, 2022 05 02.
Artigo
em Inglês
| MEDLINE | ID: mdl-35412314
3.
Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.
J Comput Aided Mol Des
; 36(6): 443-457, 2022 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-35618861
4.
Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling.
Molecules
; 27(2)2022 Jan 17.
Artigo
em Inglês
| MEDLINE | ID: mdl-35056884
5.
Machine Learning Models for Human In Vivo Pharmacokinetic Parameters with In-House Validation.
Mol Pharm
; 18(12): 4520-4530, 2021 12 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-34758626
6.
Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome.
J Comput Aided Mol Des
; 34(1): 1-10, 2020 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-31792884
7.
Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome.
J Comput Aided Mol Des
; 33(6): 559-572, 2019 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-30915709
8.
Identifying Promiscuous Compounds with Activity against Different Target Classes.
Molecules
; 24(22)2019 Nov 18.
Artigo
em Inglês
| MEDLINE | ID: mdl-31752252
9.
Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes.
Molecules
; 23(10)2018 Sep 23.
Artigo
em Inglês
| MEDLINE | ID: mdl-30249057
10.
Data-Driven Global Assessment of Protein Kinase Inhibitors with Emphasis on Covalent Compounds.
J Med Chem
; 66(11): 7657-7665, 2023 06 08.
Artigo
em Inglês
| MEDLINE | ID: mdl-37212701
11.
Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions.
CPT Pharmacometrics Syst Pharmacol
; 12(1): 122-134, 2023 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-36382697
12.
Machine Learning in Chemoinformatics and Medicinal Chemistry.
Annu Rev Biomed Data Sci
; 5: 43-65, 2022 08 10.
Artigo
em Inglês
| MEDLINE | ID: mdl-35440144
13.
Structure- and Similarity-Based Survey of Allosteric Kinase Inhibitors, Activators, and Closely Related Compounds.
J Med Chem
; 65(2): 922-934, 2022 01 27.
Artigo
em Inglês
| MEDLINE | ID: mdl-33476146
14.
Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.
CPT Pharmacometrics Syst Pharmacol
; 11(12): 1560-1568, 2022 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-36176050
15.
Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature.
ACS Chem Biol
; 17(7): 1733-1744, 2022 07 15.
Artigo
em Inglês
| MEDLINE | ID: mdl-35793809
16.
Systematic comparison of competitive and allosteric kinase inhibitors reveals common structural characteristics.
Eur J Med Chem
; 214: 113206, 2021 Mar 15.
Artigo
em Inglês
| MEDLINE | ID: mdl-33540355
17.
Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis.
ACS Omega
; 6(49): 33293-33299, 2021 Dec 14.
Artigo
em Inglês
| MEDLINE | ID: mdl-34926881
18.
Data set of competitive and allosteric protein kinase inhibitors confirmed by X-ray crystallography.
Data Brief
; 35: 106816, 2021 Apr.
Artigo
em Inglês
| MEDLINE | ID: mdl-33604432
19.
Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning.
J Cheminform
; 12(1): 36, 2020 May 24.
Artigo
em Inglês
| MEDLINE | ID: mdl-33431025
20.
Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes.
J Med Chem
; 63(16): 8738-8748, 2020 08 27.
Artigo
em Inglês
| MEDLINE | ID: mdl-31469557