Related Experiment Videos
Artificial intelligence approaches for rational drug design and discovery
Włodzisław Duch1, Karthikeyan Swaminathan, Jarosław Meller
1Department of Informatics, Nicolaus Copernicus University, Grudziadzka 5, Toruń, Poland. wduch@is.umk.pl
Abstract:
Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.
Related Concept Videos
Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Drug Discovery: Overview
Biopharmaceutical Factors Influencing Drug Product Design: Overview
Pharmacogenomics: Identification of New Drug Targets
Pharmacokinetic–Pharmacodynamic Relationship: Problems
Principles of Drug Action
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...