Machine learning applications in drug development

Computational and Structural Biotechnology Journal

Andrée Delahaye-Duriez¹  Emilie Kaufmann²  Clémence Réda³ 

[1] Corresponding author at: NeuroDiderot, UMR 1141, Inserm, Université de Paris, Sorbonne Paris Cité, Hôpital Robert Debré, 48, boulevard Sérurier, Paris 75019, France (A. Delahaye-Duriez).;Université Paris Diderot, Université de Paris, Sorbonne Paris Cité, 5, rue Thomas Mann, Paris 75013, France;NeuroDiderot, UMR 1141, Inserm, Université de Paris, Sorbonne Paris Cité, Hôpital Robert Debré, 48, boulevard Sérurier, Paris 75019, France;

关键词: Drug discovery;    Drug repurposing;    Multi-armed bandit;    Collaborative filtering;    Bayesian optimization;    Adaptive clinical trial;   

DOI  :  

来源: DOAJ

【 摘 要 】

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.

【 授权许可】

Unknown