Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform

被引：8

作者：

Guedj, Mickael ^{[1
]}

Swindle, Jack ^{[2
]}

Hamon, Antoine ^{[2
]}

Hubert, Sandra ^{[1
]}

Desvaux, Emiko ^{[1
]}

Laplume, Jessica ^{[1
]}

Xuereb, Laura ^{[1
]}

Lefebvre, Celine ^{[1
]}

Haudry, Yannick ^{[1
]}

Gabarroca, Christine ^{[1
]}

Aussy, Audrey ^{[1
]}

Laigle, Laurence ^{[1
]}

Dupin-Roger, Isabelle ^{[1
]}

Moingeon, Philippe ^{[1
]}

机构：

[1] Servier, Res & Dev, Suresnes, France

[2] Res & Dev, Lincoln, Boulogne, France

来源：

EXPERT OPINION ON DRUG DISCOVERY | 2022年 / 17卷 / 08期

关键词：

Drug discovery; target identification; data integration; artificial intelligence; multi-omics; computing platform; Computational Precision Medicine; DEVELOPMENT PRODUCTIVITY; CONNECTIVITY MAP; DATABASE; GENE; EXPRESSION; KNOWLEDGEBASE; INFORMATION; INFERENCE; MEDICINE; ONTOLOGY;

D O I：

10.1080/17460441.2022.2095368

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Introduction As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named 'Patrimony' was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. Areas covered Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. Expert opinion We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

引用

页码：815 / 824

页数：10