通过最大化包含最佳值的概率来进行批量贝叶斯优化
Jenna Fromer1, Runzhong Wang1, Mrunali Manjrekar1
1Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
我们介绍了qPO,这是一个用于批量贝叶斯优化的新策略,可以在大型库中有效地找到最佳化合物. 这种方法通过最大限度地提高选择最佳候选人的概率来增强分子设计.
科学领域:
- 计算化学计算化学
- 机器学习 机器学习
- 药物发现 药物发现 药物发现
背景情况:
- 批量贝叶斯优化 (BO) 通过高效选大型化学库来加速分子设计.
- 当前的采购策略平衡了勘探和开发,通常需要对复杂的批次函数进行近似计算.
研究的目的:
- 开发一种新的收购策略,用于分离优化批量BO.
- 提高识别高性能化合物的效率和有效性.
主要方法:
- 拟议的qPO (最佳性的多点概率),是一种利用动机的收购策略.
- 批量优化表达为个别采购得分的总和,简化了优化.
- 从平行普森采样中区分了qPO,并分析了其隐含的多样性.
主要成果:
- qPO绕过了优化批量采集功能的组合挑战.
- 经验证据表明,qPO与最先进的批量BO方法具有竞争力,并且是其补充.
- 在大型化学图书馆的模型引导探索中证明了成功的应用.
结论:
- qPO提供了一种有效的方法,用于在离散分批贝叶斯优化中纯粹利用.
- 该策略通过有效识别最佳化合物来增强分子设计.
- qPO提供了一个有价值的替代品,并补充现有的批量BO采购方法.
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