Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
This work addresses the challenge of designing optimized molecules with limited evaluations, which is crucial for drug discovery and materials science, representing an incremental improvement over existing methods.
The paper tackles the problem of sample inefficiency and distribution shift in generative molecular optimization by introducing Joint Self-Improvement, which uses a joint generative-predictive model and self-improving sampling scheme to outperform state-of-the-art methods under limited evaluation budgets.
Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yielding sample efficiency essential. Additionally, surrogate models introduced to predict molecule evaluations, suffer from distribution shift as optimization drives candidates increasingly out-of-distribution. To address these challenges, we introduce Joint Self-Improvement, which benefits from (i) a joint generative-predictive model and (ii) a self-improving sampling scheme. The former aligns the generator with the surrogate, alleviating distribution shift, while the latter biases the generative part of the joint model using the predictive one to efficiently generate optimized molecules at inference-time. Experiments across offline and online molecular optimization benchmarks demonstrate that Joint Self-Improvement outperforms state-of-the-art methods under limited evaluation budgets.