Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark

2026-06-29

AI Summary

Existing molecular generation methods rely heavily on surrogate metrics and pretraining data from drug discovery, limiting their generalizability to structurally distinct domains such as nanotechnology. This work introduces the Nanomaterial Molecular Optimization (NMO) benchmark, which, for the first time, employs quantum simulations as a ground-truth objective function and incorporates structural constraints alongside domain-agnostic pretraining strategies to formulate a generative task with a rugged fitness landscape. By establishing a new paradigm that balances scientific utility with rigorous model evaluation, the proposed framework not only surpasses state-of-the-art physical performance metrics through its baseline methods but also uncovers novel structural motifs, exposes limitations of advanced optimization algorithms in this emerging domain, and demonstrates the potential of machine learning to drive authentic scientific discovery.

Abstract

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.

Problem

• generative molecular design
• transferability
• nanotechnology
• quantum materials science
• molecular optimization

Innovation

• Nanotechnology Molecular Optimization
• quantum simulation
• structural constraints
• domain-agnostic pretraining
• generative molecular design