每个化合物都是候选者:经验引导的冒险方法,以加速小分子药物发现
Dermot F McGinnity1, Jerome Meneyrol2, Christophe Boldron2
1Aptuit (Verona) Srl, an Evotec Company, Via Alessandro Fleming 4, 37135 Verona, Italy.
Drug discovery today
|April 10, 2025
概括
人工智能和机器学习加速了小分子药物发现,但改进的翻译预测能力和"每个化合物都是候选药物"战略对于在两年内提供候选药物至关重要.
科学领域:
- 药物发现 药物发现 药物发现
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 小分子药物发现是一个缓慢而昂贵的过程.
- 人工智能 (AI) 和机器学习 (ML) 提供了潜在的解决方案,但单独是不够的.
- 目前的行业候选人交付时间表很长,平均为4.0年.
研究的目的:
- 突出在药物发现中改进翻译预测性的必要性.
- 倡导战略转向"每个化合物都是候选者"的方法.
- 将候选药物交付时间缩短到两年以下.
主要方法:
- 在药物发现工作流程中利用AI和ML.
- 优化流程和决策,以更快地处理数据 (5天).
- 分析下游的体外和体内模型数据以获得翻译性见解.
主要成果:
- 与行业平均水平相比,在合作组合中实现了2.9年的候选人交付时间表.
- 证明了经验主导的冒险的重要性.
- 通过化合物进展分析确定了关键阻塞剂和转化值.
结论:
- AI/ML对于有效的药物发现至关重要,但不够.
- 一个"每个化合物都是候选物"的策略,加上改进的翻译预测性,可以显著缩短时间表.
- 从所有化合物中积极学习的思维转变是未来成功的关键.
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