Active Learning of Causal Relationships to Optimize Molecular Design with Targeted Interventions

An active learning approach that discerns underlying cause-effect relationships through strategic sampling can identify the smallest subset of a 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.