Extrapolative Analyses for Reliable Machine Learning Driven Scientific Discovery

从实验和观测来源产生和分析数字数据方面出现了有希望的爆炸式增长，这为机器学习（ML）驱动的科学发现在化学（化学信息学）和生物学（生物信息学）等高影响力应用中提供了许多机会。不幸的是，目前的机器学习方法经常不能正确地描述与训练期间所看到的明显不同的数据（即外推）。这反过来又阻碍了我们做出真正超越现有知识的科学发现的能力。例如，这在化学虚拟筛选活动中具有重要意义，人们希望使用ML预测来指导昂贵的现实世界实验的潜在目标（例如，在药物发现应用中）。ML模型的外推能力差会导致假阳性，通过昂贵的合成和实验测试新化学实体浪费时间和资源。该奖项的工作将提高机器学习模型在科学领域的实际效用，并防止模型预测的错误使用。该项目还为研究生提供研究培训机会。该项目开发了各种方法，以更准确地评估机器学习预测在新输入上的可靠性，并提高模型的外推能力。首先，该项目开发了经验试验，以更准确地评估机器学习模型拟合过程在超出训练集分布支持的领域上的外推能力。其次，利用外推评估，该项目开发技术来彻底探索可能外推的输入空间，以预测和过滤掉可能不可靠的预测。最后，该项目构建方法来指导新的训练数据的获取，一旦训练，将改进模型外推。该奖项由数学科学部颁发，由国家科学基金会高级网络基础设施办公室联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There has been a promising explosion in the production and analysis of digital data from experimental and observational sources, which presents many opportunities for machine learning (ML) driven scientific discovery in high-impact applications such as chemistry (cheminformatics) and biology (bioinformatics). Unfortunately, current ML methodology often fails to properly characterize data markedly distinct from what was seen during training (i.e., extrapolation). This, in turn, hampers our ability to make scientific discoveries that truly extend past our current knowledge. For example, this is of great consequence in chemical virtual screening campaigns, where one hopes to use ML predictions to guide potential targets for expensive real-world experimentation (e.g., in drug discovery applications). Poor extrapolative power of ML models can result in false positives, wasting time and resources through costly synthesis and experimental testing of novel chemical entities. The work stemming from this award will improve the real-world utility of ML models in scientific domains and prevent the faulty use of model predictions. The project also provides research training opportunities for graduate students. This project develops various methodologies to more accurately assess the reliability of ML predictions on novel inputs and improve models' extrapolatory capabilities. First, the project develops empirical trials to more accurately evaluate the extrapolative capabilities of ML model fitting procedures on domains that lie beyond the training set distributional support. Second, utilizing extrapolative assessments, the project develops techniques to thoroughly explore the input space of possible extrapolation to anticipate and filter out likely unreliable predictions. Lastly, the project builds methodology to guide the acquisition of new training data that, once trained on, will improve model extrapolation.This award by the Division of Mathematical Sciences is jointly supported by the NSF Office of Advanced Cyberinfrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.