Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions

arXiv:2404.04224v1 Announce Type: new
Abstract: 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 …

abstract arxiv become causal challenges complexities cs.lg current datasets decoding deep learning design environmental face machine machine learning management materials materials science medicine physics.chem-ph physics.data-an predictions q-bio.bm science standard tasks type