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 …
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 …
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 …
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 …
[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 …
Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design
QS Du, RB Huang, KC Chou - Current protein and peptide …, 2008 - ingentaconnect.com
This review is to summarize three new QSAR (quantitative structure-activity relationship)
methods recently developed in our group and their applications for drug design. Based on …
methods recently developed in our group and their applications for drug design. Based on …
Machine learning in drug discovery
G Klambauer, S Hochreiter… - Journal of chemical …, 2019 - ACS Publications
QSAR. Despite this long tradition, machine learning methods gained substantial momentum
recently triggered by the success of deep learning in many application areas. 1 A wide …
recently triggered by the success of deep learning in many application areas. 1 A wide …
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce
the high cost and time of drug discovery. After more than five decades of continuing …
the high cost and time of drug discovery. After more than five decades of continuing …
From machine learning to deep learning: progress in machine intelligence for rational drug discovery
Highlights•Six commonly used machine learning methods in QSAR models are
summarized.•Newly developed combinatorial QSAR and hybrid QSAR methods are …
summarized.•Newly developed combinatorial QSAR and hybrid QSAR methods are …
Descriptor free QSAR modeling using deep learning with long short-term memory neural networks
SK Chakravarti, SRM Alla - Frontiers in artificial intelligence, 2019 - frontiersin.org
Current practice of building QSAR models usually involves computing a set of descriptors for
the training set compounds, applying a descriptor selection algorithm and finally using a …
the training set compounds, applying a descriptor selection algorithm and finally using a …