通过高吞吐量人工智能驱动的平台改变药物发现:与Patrimony有5年的经验
François-Xavier Blaudin de Thé1, Claire Baudier1, Renan Andrade Pereira2
1Servier Research & Development, Saclay, France.
Drug discovery today
|September 17, 2023
概括
服务器服务器服务器
科学领域:
- 计算机化药物发现.
- 医学中的人工智能.
- 免疫炎症和神经系统疾病.
背景情况:
- 高通量计算平台加速了药物发现.
- 塞维尔开发了 Patrimony 平台,使用计算科学和人工智能集成多式联网数据.
- 该平台有助于优先考虑免疫炎症疾病的治疗目标.
研究的目的:
- 分享关于产业化遗产平台的经验.
- 讨论扩大平台对神经疾病的应用.
- 突出平台工业化的挑战和成功因素.
主要方法:
- 利用计算科学和人工智能 (AI).
- 整合来自内部和外部来源的大量多式联运数据.
- 将平台应用于免疫-炎症和神经系统疾病.
主要成果:
- 遗产平台可以对治疗目标进行优先排序.
- 研究人员对免疫炎症疾病病理生理学有了更深入的了解.
- 该平台的应用已成功扩展到神经疾病.
结论:
- 像Patrimony这样的计算平台的工业化带来了挑战和关键的成功因素.
- 将这些平台整合到端到端的药物发现中至关重要.
- 人类专家的早期参与对于改变药物发现影响至关重要.
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