MOTIVATION: One of the most successful methods for predicting the properties of chemical compounds is the quantitative structure-activity relationship (QSAR) methods. The prediction accuracy of QSAR models has recently been greatly improved by employ...
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...
A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole-based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effecti...
SAR and QSAR in environmental research
May 1, 2019
The application of machine learning methods to the construction of quantitative structure-activity relationship models is a complex computational problem in which dimensionality reduction of the representation of the molecular structure plays a funda...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2019
Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to ...
Quantitative structure-activity relationship (QSAR) techniques, especially those that possess three-dimensional attributes, such as the comparative molecular field analysis (CoMFA), are frequently used in modern-day drug design and other related rese...
Anti-cancer agents in medicinal chemistry
Jan 1, 2019
Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal ...
Toxicological sciences : an official journal of the Society of Toxicology
Aug 1, 2018
Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to ou...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.