科学家们搞了个新基准NMO，专门用来测试分子优化算法在纳米技术上的表现，直接用量子模拟打分，结果发现之前的先进方法还不如简单方法好用。
新发布的Nanotechnology Molecular Optimization (NMO) Benchmark 替代传统药物发现中的代理指标，使用量子模拟评分。该基准对生成式分子模型提出严格结构约束和崎岖适应度景观。测试发现先进的分子优化方法在NMO任务上表现不及更简单的基线方法。研究团队开发了新基线方法，包含用于建模结构约束的新表示和消除制药数据集偏见的域无关预训练策略。该方法超越了最先进的物理性质结果，并揭示此前未知的结构基序。
Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets. This combination yields strong benchmark metrics but limits transferability to domains structurally distinct from drug discovery. To overcome this limitation and drive discovery toward real, scientifically grounded targets, we introduce the Nanotechnology Molecular Optimization (NMO) Benchmark, which bridges machine learning (ML) and quantum materials science. NMO acts simultaneously as a rigorous testbed for the ML community and a discovery engine for nanotechnology research. The suite replaces proxy oracles with quantum simulations and introduces strict protocols that prioritize scientific utility over leaderboard-oriented overfitting. The physics-based NMO tasks impose hard structural constraints and rugged fitness landscapes, posing fundamentally new requirements on generative models. Notably, advanced molecular optimization methods underperform much simpler approaches on the NMO tasks. We develop a new baseline method identifying the critical components to solve the NMO tasks, including a novel representation for modeling structural constraints and a domain-agnostic pretraining strategy to eliminate pharmaceutical dataset bias. Our results surpass state-of-the-art physical properties and reveal previously unknown structural motifs, offering new insights for the nanotechnology community and demonstrating that ML can drive genuine scientific discovery.