超越药物发现：纳米技术分子优化（NMO）基准

摘要

生成式分子设计目前依赖于针对类药性质的简单代理基准以及在大规模制药数据集上预训练的模型。这种组合虽然能产生强大的基准指标，但限制了向与药物发现结构不同领域的可迁移性。为突破这一局限，推动发现走向真实且具有科学依据的目标，我们提出了纳米技术分子优化（NMO）基准，该基准架起了机器学习（ML）与量子材料科学之间的桥梁。NMO既充当了机器学习领域的严格测试平台，也作为纳米技术研究的发现引擎。该套件用量子模拟替代了代理预言机，并引入了严格的协议，优先考虑科学实用性而非以排行榜为导向的过拟合。基于物理的NMO任务施加了严格的结构约束和崎岖的适应度景观，对生成模型提出了全新的要求。值得注意的是，先进的分子优化方法在NMO任务上的表现远不如更简单的方法。我们开发了一种新的基线方法，识别出了解决NMO任务的关键组件，包括用于建模结构约束的新型表示和消除制药数据集偏差的领域无关预训练策略。我们的结果超越了最先进的物理性质，并揭示了以前未知的结构基序，为纳米技术社区提供了新见解，证明了机器学习能够驱动真正的科学发现。

English

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.