Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine

•AI allows to integrate massive multi-modal data to build up predictive models.•Modelling of complex heterogeneous diseases allows to identify therapeutic targets.•AI facilitates the design, selection and repurposing of drugs interacting with targets.•AI drives the emergence of a computational preci...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Drug discovery today 2022-01, Vol.27 (1), p.215-222
Hauptverfasser: Moingeon, Philippe, Kuenemann, Mélaine, Guedj, Mickaël
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•AI allows to integrate massive multi-modal data to build up predictive models.•Modelling of complex heterogeneous diseases allows to identify therapeutic targets.•AI facilitates the design, selection and repurposing of drugs interacting with targets.•AI drives the emergence of a computational precision medicine. Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.
ISSN:1359-6446
1878-5832
DOI:10.1016/j.drudis.2021.09.006