Machine Learning Models for Lipophilicity and Their Domain of Applicability-Reference-Cited by-同舟云学术

Machine Learning Models for Lipophilicity and Their Domain of Applicability

Published:2007-07-19 Issue:4 Volume:4 Page:524-538
ISSN:1543-8384
Container-title:Molecular Pharmaceutics
language:en
Short-container-title:Mol. Pharmaceutics

Author:

Schroeter Timon¹,Schwaighofer Anton¹,Mika Sebastian¹,Ter Laak Antonius¹,Suelzle Detlev¹,Ganzer Ursula¹,Heinrich Nikolaus¹,Müller Klaus-Robert¹

Affiliation:

1. Fraunhofer FIRST, Kekuléstrasse 7, 12489 Berlin, Germany, Department of Computer Science, Technische Universität Berlin, Franklinstrasse 28/29, 10587 Berlin, Germany, idalab GmbH, Sophienstrasse 24, 10178 Berlin, Germany, and Research Laboratories of Bayer Schering Pharma AG, Müllerstrasse 178, 13342 Berlin, Germany

Publisher

American Chemical Society (ACS)

Subject

Drug Discovery,Pharmaceutical Science,Molecular Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/mp0700413

Reference34 articles.

1. logD7.4 Modeling Using Bayesian Regularized Neural Networks. Assessment and Correction of the Errors of Prediction

2. Gaussian Processes for Machine Learning

3. Schroeter, T.; Schwaighofer, A.; Mika, S.; Ter Laak, A.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Müller, K.R. Predicting lipophilicity of drug discovery molecules using gaussian process models.ChemMedChem. URL: http://dx.doi.org/10.1002/cmdc.200700041.

4. Schroeter, T.; Schwaighofer, A.; Mika, S.; Ter Laak, A.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Müller, K.R. Estimating the domain of applicability for machine learning qsar rmodels: A study on aqueous solubility of drug discovery molecules.J. Comput.Aided Mol. Des., accepted for publication. URL: http://dx.doi.org/10.1007/s10822-007-9125-z.

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks;Journal of Chemical Information and Modeling;2024-07-08

2. Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition;Chemical Research in Toxicology;2024-03-19

3. Convolutional neural networks: Basic concepts and applications in manufacturing;Artificial Intelligence in Manufacturing;2024

4. Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium;Digital Discovery;2023

5. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives;Chemical Biology & Drug Design;2022-05-08