药物发现
计算机科学
吞吐量
过程(计算)
数据科学
生物信息学
生物
电信
操作系统
无线
作者
François-Xavier Blaudin de Thé,Claire Baudier,Renan Andrade Pereira,Céline Lefèbvre,Philippe Moingeon
标识
DOI:10.1016/j.drudis.2023.103772
摘要
High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact.
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