  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.
