OPLE:药物发现平台将二维相似性与人工智能结合起来,以预测非目标负债
Sarah E Biehn1, Juerg Lehmann1, Christoph Mueller2
1Eurofins Discovery Services North America, LLC, 6 Research Park Drive, Saint Charles, MO 63304, USA.
Pharmaceuticals (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了OPLE模型,将机器学习和分子相似性结合起来,以预测小分子药物活性,通过识别有前途的候选药物来改善早期药物发现,并减少目标外负债.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 非目标负债是药物发现的一个主要障碍,导致候选人失败.
- 人工智能 (AI) 和机器学习 (ML) 越来越多地用于加速药物发现.
- 需要预测模型来有效地识别成功的小分子候选药物.
研究的目的:
- 开发和验证OPLE (负债目标预测) 模型.
- 结合分子相似性和机器学习来预测化合物活性.
- 评估OPLE模型对药物安全责任的预测性能.
主要方法:
- 模型使用专有和公共数据进行训练,用于SafetyScreen面板18和44.
- 从扩展连接指纹 (ECFP) 中计算了二维 (2D) 坦尼莫托相似性.
- 来自ECFP相似性和ML模型的预测使用信念理论进行了结合.
主要成果:
- 一个概率分配曲线证明了相似性和活动之间的关系.
- 在80%以上的安全屏幕目标中,OPLE模型实现了超过0.8的回忆值.
- 这些模型显示出有利的预测能力,识别了活性分子并将虚假阴性最小化.
结论:
- 早期预测安全责任在小分子药物发现中至关重要.
- OPLE模型作为一个早期检测工具来节省资源.
- 这种方法有助于将努力集中在具有最小预测或测量目标外负债的候选药物上.
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