用图能量模型无监督生成满足目标性质的分子，提升药物设计效率。
Towards Efficient Molecular Property Optimization with Graph Energy Based Models
- 基于图能量模型，无需属性标签即可隐式优化分子性质。
- 在多个基准上性能超越现有方法，生成效率高且稳定。
- 适合新药研发中的分子生成任务，尤其关注高效筛选场景。
由于化学空间的广阔与复杂性，优化化学性质是一项挑战。本文提出一种生成式能量模型架构，用于隐式化学性质优化，能够高效生成满足目标性质的分子，而无需显式条件生成。该方法采用图能量模型（Graph Energy Based Models）并结合无需属性标签的训练策略。我们在多个公认的化学基准上验证了该方法，结果表明其性能优于当前最先进的方法，并在全新药物设计中展现出良好的鲁棒性与效率。
原文摘要 · Abstract (English)
Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chemical property optimization, designed to efficiently generate molecules that satisfy target properties without explicit conditional generation. We use Graph Energy Based Models and a training approach that does not require property labels. We validated our approach on well-established chemical benchmarks, showing superior results to state-of-the-art methods and demonstrating robustness and efficiency towards de novo drug design.
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