Autor según el artículo: Staszak, Maciej; Staszak, Katarzyna; Wieszczycka, Karolina; Bajek, Anna; Roszkowski, Krzysztof; Tylkowski, Bartosz;
Departamento: Enginyeria Química
Autor/es de la URV: Tylkowski, Bartosz
Palabras clave: Training data Structure (composition) Small molecules Regression Quantum models Prediction Neural networks Neural network systems Neural network Network modeling Multiclass Models Machine learning Level of abstraction Learning techniques Learning systems In-vitro Drug design Discovery Deep learning Database Computing power Classification Chemoinformatics Chemical structure Carcinogenicity Bioactivity Artificial intelligence
Resumen: The paper presents a comprehensive overview of the use of artificial intelligence (AI) systems in drug design. Neural networks, which are one of the systems employed in AI, are used to identify chemical structures that can have medical relevance. Successful training of neural networks must be preceded by the acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity. In general, a neural network requires a large set of training data, which must contain information about the chemical structure-biological activity relationship. The data can come from experimental measurements, but can also be generated using appropriate quantum models. In many of the studies presented below, authors showed a significant potential of neural networks to produce generalizations based on even relatively narrow training data. Despite the fact that neural network systems have been known for more than 40 years, it is only recently that they have seen rapid development due to the wider availability of computing power. In recent years, there has been a growing interest in deep learning techniques, bringing network modeling to a new level of abstraction. Deep learning allows combining what seems to be causally distant phenomena and effects, and to associate facts in a way resembling the human mind. This article is categorized under: Computer and Information Science > Chemoinformatics
Áreas temáticas: Physical and theoretical chemistry Mathematical & computational biology Materials chemistry Computer science applications Computational mathematics Chemistry, multidisciplinary Biochemistry
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: bartosz.tylkowski@urv.cat
Identificador del autor: 0000-0002-4163-0178
Fecha de alta del registro: 2024-09-07
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1568
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Wiley Interdisciplinary Reviews-Computational Molecular Science. 12 (2):
Referencia de l'ítem segons les normes APA: Staszak, Maciej; Staszak, Katarzyna; Wieszczycka, Karolina; Bajek, Anna; Roszkowski, Krzysztof; Tylkowski, Bartosz; (2022). Machine learning in drug design: Use of artificial intelligence to explore the chemical structure-biological activity relationship. Wiley Interdisciplinary Reviews-Computational Molecular Science, 12(2), -. DOI: 10.1002/wcms.1568
DOI del artículo: 10.1002/wcms.1568
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV