AIMC Topic: Quantitative Structure-Activity Relationship

Clear Filters Showing 501 to 510 of 551 articles

Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology
Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods ha...

An Analysis of QSAR Research Based on Machine Learning Concepts.

Current drug discovery technologies
Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions...

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.

Chemical biology & drug design
Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analyti...

VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models.

Bioinformatics (Oxford, England)
SUMMARY: Although many quantitative structure-activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualiza...

Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances.

Advances in experimental medicine and biology
Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identificatio...

[AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
Toxicity testing is critical for new drug and chemical development process. A clinical study, experimental animal models, and in vitro study are performed to evaluate the safety of a new drug. The limitations of these methods include extensive time f...

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods.

Mini reviews in medicinal chemistry
BACKGROUND: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applicati...