Policy-Based Active Learning for Efficient Molecular Identification.

Efficient identification of high-value molecules under limited experimental budgets remains a central challenge in automated chemical design. In Level 4 automation settings, where chemists define candidate spaces and machine learning models guide experimental selection, pool-based active learning has emerged as a practical framework for prioritizing compounds. However, conventional approaches primarily optimize predictive accuracy of surrogate structure-function models, which may not directly align with the objective of maximizing search efficiency toward identifying the molecule with the most favorable property value within a predefined candidate pool. We propose a policy-based active learning framework that reformulates molecular pool selection as a sequential decision-making problem. The iterative selection process is modeled as a Markov decision process, and a policy network is trained to optimize cumulative search performance under constrained evaluation budgets. Property prediction models are incorporated as contextual signals rather than optimization targets, enabling direct optimization of search efficiency. We construct 1409 molecular identification tasks derived from MoleculeNet and ChEMBL. In addition, six literature-curated in vivo tasks are constructed to assess performance. Across both benchmark and in vivo settings, the framework demonstrates improved efficiency in identifying optimal molecules within limited evaluation cycles. Case studies further illustrate the strengths and limitations of the framework. These results highlight the potential of policy-driven active learning to enhance molecular identification efficiency in predefined candidate pools, offering a generalizable strategy for budget-constrained chemical discovery.