超越藥物發現：奈米科技分子優化（NMO）基準

摘要

生成式分子設計受到藥物相似性簡單代理基準以及在大規模藥物資料集上預訓練的模型所影響。這種組合產生了強大的基準指標，但限制了向與藥物發現結構截然不同的領域的遷移能力。為克服此限制，並推動研究朝向真實且具科學基礎的目標邁進，我們引入了奈米技術分子優化（NMO）基準，該基準連繫了機器學習（ML）與量子材料科學。NMO同時作為ML社群嚴謹的測試平台，以及奈米技術研究的發現引擎。此套件以量子模擬取代代理預測器，並引入嚴格協議，優先考量科學實用性而非以排行榜為導向的過擬合。基於物理的NMO任務施加了嚴格的結構限制與崎嶇的適應度景觀，對生成模型提出了全新的要求。值得注意的是，先進的分子優化方法在NMO任務上的表現不如更簡單的方法。我們開發了一種新的基線方法，識別出解決NMO任務的關鍵組件，包括用於建模結構限制的新型表徵，以及消除藥物資料集偏誤的領域無關預訓練策略。我們的結果超越了最先進的物理性質，並揭示了先前未知的結構模體，為奈米技術社群提供了新的見解，並證明了ML能夠推動真正的科學發現。

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.