Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Summary
The Nanotechnology Molecular Optimization (NMO) Benchmark introduces physics-based molecular design tasks replacing drug-discovery-focused metrics, aiming to drive scientific discovery in nanotechnology. The paper shows that advanced methods underperform simpler approaches on NMO, and proposes new baseline methods including a novel representation and domain-agnostic pretraining.
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Paper page - Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Source: https://huggingface.co/papers/2606.30170
Abstract
The Nanotechnology Molecular Optimization (NMO) Benchmark introduces physics-based molecular design challenges that require new generative model approaches, moving beyond drug-discovery-focused metrics to enable scientific discovery in nanotechnology.
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 theNanotechnology 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 withquantum simulationsand introduces strict protocols that prioritize scientific utility over leaderboard-oriented overfitting. The physics-based NMO tasks impose hardstructural constraintsand ruggedfitness landscapes, posing fundamentally new requirements ongenerative models. Notably, advancedmolecular optimizationmethods 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 modelingstructural constraintsand adomain-agnostic pretrainingstrategy to eliminatepharmaceutical 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.
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