The content discusses an active learning approach for decoding chemical complexities and optimizing molecular design. Key highlights:
Most current machine learning and deep learning methods face challenges when applied across different datasets due to their reliance on correlations between molecular representation and target properties. These approaches typically require large datasets to capture the diversity within the chemical space.
The authors introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling using 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 of molecules with desired properties, such as a large dipole moment. This is demonstrated on the QM9 dataset.
The active learning algorithm iteratively constructs a minimal dataset that accurately reproduces the causal relationships between molecular features and the target property, as represented by a global causal graph. This is achieved by comparing the causal graphs of candidate datasets to the global graph using a graph distance metric.
The authors show that the actively learned dataset converges to the global causal graph more quickly and with less noise compared to randomly selected data. The predictive performance on the target property is similar for both active and random datasets.
Using the actively learned causal model, the authors demonstrate how to perform targeted interventions on molecular features to design molecules with high dipole moments. They search a reference dataset to find realistic molecules with the desired intervened features.
The causal analysis provides insights into the influence of various molecular features, such as the presence of NH, OH bonds, molecular weight, atomic charges, and electronegativity, on the dipole moment of molecules.
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arxiv.org
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