paperarXivTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago
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 f
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- LinkedLinked via unknownData-driven surrogates of rational design enable antimicrobial peptide optimization →
- LinkedLinked via unknownHow a Google DeepMind Spin-off Hunts Hidden Drug Targets →
- LinkedLinked via unknownFrom virtual experiments to biomedical insight with synthetic data →
- LinkedLinked via unknownA near-autonomous AI chemist improves a challenging reaction in medicinal chemistry →
- LinkedLinked via unknownEmpowering biomedical evidence exploration and synthesis with deep knowledge graph research →
- PossiblePossibly related (embedding) · 52%deepmodeling/DeePTB →
Covers
newsData-driven surrogates of rational design enable antimicrobial peptide optimizationnewsHow a Google DeepMind Spin-off Hunts Hidden Drug TargetsnewsBridging three-dimensional molecular structures and artificial intelligence with a conformation description languagenewsFrom virtual experiments to biomedical insight with synthetic datanewsA near-autonomous AI chemist improves a challenging reaction in medicinal chemistry
Covers (incoming)
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Implements (incoming)
Related across the graph
newsHow a Google DeepMind Spin-off Hunts Hidden Drug Targetsrepodralgroup/mlatomnewsData-driven surrogates of rational design enable antimicrobial peptide optimizationnewsEfficient and valid large molecule generation via self-supervised generative models - NaturenewsAI-Driven Lead Optimization in Medicinal Chemistry - Drug Discovery NewsnewsFrom virtual experiments to biomedical insight with synthetic datanewsGuiding generative models to uncover diverse and novel crystals via reinforcement learningnewsScientists’ Side Hustle? Using AI and Quantum Computing to Generate New Peptidesrepordk/p2ranknewsJMIR report: machine learning accelerates radiopharmaceutical drug discovery, optimizes personalized dosimetry - EurekAlert!newsArtificial intelligence in oncology drug discovery: from target identification to therapeutic molecule generation - EurekAlert!newsEmpowering biomedical evidence exploration and synthesis with deep knowledge graph researchnewsA near-autonomous AI chemist improves a challenging reaction in medicinal chemistrynewsReshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1repojanosh/matbench-discoverynewsVibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - Naturerepoelectrocatalysis-group/atomic-recipesnewsAI-powered target identification: How machine learning is finding new drug targets - Drug Discovery NewsnewsMachine learning maps the atomic complexity inside batteries - NanowerknewsNew Machine Learning Method Optimizes Production of Atom-Thin Filters - AZoMnewsMachine Learning Accelerates Search for Longer-Lasting Materials for Solar Cells - UC Davis College of EngineeringnewsBridging three-dimensional molecular structures and artificial intelligence with a conformation description languagenewsMachine learning for drug metabolism prediction: CYPs, Phase II enzymes, and metabolite ID - Drug Discovery Newsrepojeinlee1991/chinese-llm-benchmarknewsAccelerating sustainable glass discovery: integrating molecular dynamics, machine learning, and robotic synthesis - Naturerepodeepmodeling/DeePTB