Sample Efficient Generative Molecular Optimization with Joint Self-Improvement

Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, evaluating candidate molecules is expensive, so sample efficiency, i.e., generating optimized molecules within a fixed evaluation budget, is essential. Moreover, in the offline setting, where a surrogate model approximates candidate evaluation, sample inefficiency compounds with surrogate error, further limiting performance. To address these challenges, we introduce Joint Self-Improvement (JSI), a framework for sample-efficient online and offline molecular optimization. JSI combines (i) a joint generative-predictive model trained via a joint likelihood-based loss, which aligns molecule generation and surrogate modeling, and (ii) inference-time self-improvement, which iteratively biases the generative component of the joint model toward higher-scoring molecules using direct objective evaluations in the online setting and the predictive component in the offline setting. Experiments across docking score optimization, in both online and offline settings, as well as antimicrobial peptide optimization, demonstrate that JSI outperforms state-of-the-art methods under limited evaluation budgets.