通过主动因果学习解码复杂化学物质,进行有针对性的干预

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zachary R Fox, Ayana Ghosh
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引用次数: 0

摘要

根据分子结构预测和增强固有特性对于医学、材料科学和环境管理领域的设计任务至关重要。目前大多数机器学习和深度学习方法已成为预测的标准,但由于依赖分子表征和目标特性之间的相关性,它们在应用于不同数据集时面临挑战。这些方法通常依赖于大型数据集来捕捉化学空间内的多样性,从而有助于更准确地近似、内插或外推分子的化学行为。在我们的研究中,我们引入了一种主动学习方法,通过使用图损失函数进行策略性采样来辨别潜在的因果关系。这种方法能识别出数据集的最小子集,该子集能够编码代表更大化学空间的最多信息。然后,利用确定的因果关系进行系统干预,在模型以前未曾接触过的化学空间内优化设计任务。虽然我们的实施侧重于 QM9 量子化学数据集的特定设计任务--寻找具有大偶极矩的分子--但我们的主动因果学习方法在智能采样和干预的驱动下,有望在分子、材料设计和发现领域得到更广泛的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active causal learning for decoding chemical complexities with targeted interventions
Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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