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 …
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 …
An analysis of QSAR research based on machine learning concepts
MR Keyvanpour, MB Shirzad - Current Drug Discovery …, 2021 - ingentaconnect.com
Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to
correlate chemical molecules with their biological activities based on their chemical …
correlate chemical molecules with their biological activities based on their chemical …
AutoQSAR: an automated machine learning tool for best-practice quantitative structure–activity relationship modeling
Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate
and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results …
and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results …
Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery
I Ponzoni, V Sebastián-Pérez, C Requena-Triguero… - Scientific reports, 2017 - nature.com
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …
constitutes a complex computational problem, where the identification of the most …
A deep learning-based chemical system for QSAR prediction
Research on quantitative structure-activity relationships (QSAR) provides an effective
approach to determine new hits and promising lead compounds during drug discovery. In …
approach to determine new hits and promising lead compounds during drug discovery. In …
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) …
QSAR without borders
EN Muratov, J Bajorath, RP Sheridan… - Chemical Society …, 2020 - pubs.rsc.org
Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …
important applications of statistical and more recently, machine learning and artificial …
Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery
YL Wang, F Wang, XX Shi, CY Jia, FX Wu… - Briefings in …, 2021 - academic.oup.com
Effective drug discovery contributes to the treatment of numerous diseases but is limited by
high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method …
high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method …
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets
Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …
quantitative structure–activity relationships (QSARs), there is no agreed single best …