Practical guidelines for the use of gradient boosting for molecular property prediction
Decision tree ensembles are among the most robust, high-performing and computationally
efficient machine learning approaches for quantitative structure–activity relationship (QSAR) …
efficient machine learning approaches for quantitative structure–activity relationship (QSAR) …
Comprehensive ensemble in QSAR prediction for drug discovery
Background Quantitative structure-activity relationship (QSAR) is a computational modeling
method for revealing relationships between structural properties of chemical compounds …
method for revealing relationships between structural properties of chemical compounds …
Ensemble machine learning approach for quantitative structure activity relationship based drug discovery: A Review
This comprehensive review explores the pivotal role of ensemble machine learning
techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug …
techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug …
Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
X Li, D Fourches - Journal of Cheminformatics, 2020 - Springer
Deep neural networks can directly learn from chemical structures without extensive, user-
driven selection of descriptors in order to predict molecular properties/activities with high …
driven selection of descriptors in order to predict molecular properties/activities with high …
Extreme gradient boosting as a method for quantitative structure–activity relationships
In the pharmaceutical industry it is common to generate many QSAR models from training
sets containing a large number of molecules and a large number of descriptors. The best …
sets containing a large number of molecules and a large number of descriptors. The best …
General approach to estimate error bars for quantitative structure–activity relationship predictions of molecular activity
R Liu, KP Glover, MG Feasel… - Journal of Chemical …, 2018 - ACS Publications
Key requirements for quantitative structure–activity relationship (QSAR) models to gain
acceptance by regulatory authorities include a defined domain of applicability (DA) and …
acceptance by regulatory authorities include a defined domain of applicability (DA) and …
Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models
Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory
versatility and accuracy in fields such as drug discovery because they are based on …
versatility and accuracy in fields such as drug discovery because they are based on …
[HTML][HTML] Multi-task learning models for predicting active compounds
Z Zhao, J Qin, Z Gou, Y Zhang, Y Yang - Journal of Biomedical Informatics, 2020 - Elsevier
The computational drug discovery methods can find potential drug-target interactions more
efficiently and have been widely studied over past few decades. Such methods explore the …
efficiently and have been widely studied over past few decades. Such methods explore the …
Deep neural networks for QSAR
Y Xu - Artificial intelligence in drug design, 2022 - Springer
Quantitative structure–activity relationship (QSAR) models are routinely applied
computational tools in the drug discovery process. QSAR models are regression or …
computational tools in the drug discovery process. QSAR models are regression or …
Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
Ensemble learning helps improve machine learning results by combining several models
and allows the production of better predictive performance compared to a single model. It …
and allows the production of better predictive performance compared to a single model. It …