Cargando…
Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions
Machine learning (ML) enables modeling of quantitative structure–activity relationships (QSAR) and compound potency predictions. Recently, multi-target QSAR models have been gaining increasing attention. Simultaneous compound potency predictions for multiple targets can be carried out using ensemble...
Autores principales: | Rodríguez-Pérez, Raquel, Bajorath, Jürgen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7982389/ https://www.ncbi.nlm.nih.gov/pubmed/33598870 http://dx.doi.org/10.1007/s10822-021-00376-8 |
Ejemplares similares
-
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
por: Rodríguez-Pérez, Raquel, et al.
Publicado: (2020) -
Support Vector Machine Classification and Regression
Prioritize Different Structural Features for Binary Compound Activity
and Potency Value Prediction
por: Rodríguez-Pérez, Raquel, et al.
Publicado: (2017) -
Rationalizing general limitations in assessing and comparing methods for compound potency prediction
por: Janela, Tiago, et al.
Publicado: (2023) -
Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure–Potency Fingerprint
por: Janela, Tiago, et al.
Publicado: (2023) -
Compound dataset and custom code for deep generative multi-target compound design
por: Blaschke, Thomas, et al.
Publicado: (2021)