FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
Advances and 42 challenges in deep generative models for de novo molecule generation.Wiley Interdis- ciplinary Reviews: Computational Molecular Science2019,9, e1395
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SGRPO is a GRPO-style framework that constructs set-level diversity rewards via supergroup sampling and leave-one-out redistribution to expand the utility-diversity Pareto frontier in biomolecular design tasks.
SCMDM adapts trained masked diffusion models to condition denoising steps on their own prior clean predictions, cutting generative perplexity nearly in half on open-web text while improving discretized image, molecule, and genomic synthesis.
FRIGID scales a diffusion-based model for de novo molecular structure generation from mass spectra, reaching over 18% top-1 accuracy on MassSpecGym and tripling prior bests on NPLIB1 via large unlabeled training and inference-time fragmentation refinement with log-linear compute scaling.
Generative chemical language models pretrained on general chemical data and fine-tuned on energetic materials datasets enable accelerated discovery of synthetically accessible high-performance compounds.
citing papers explorer
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FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
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Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization
SGRPO is a GRPO-style framework that constructs set-level diversity rewards via supergroup sampling and leave-one-out redistribution to expand the utility-diversity Pareto frontier in biomolecular design tasks.
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Simple Self-Conditioning Adaptation for Masked Diffusion Models
SCMDM adapts trained masked diffusion models to condition denoising steps on their own prior clean predictions, cutting generative perplexity nearly in half on open-web text while improving discretized image, molecule, and genomic synthesis.
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FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
FRIGID scales a diffusion-based model for de novo molecular structure generation from mass spectra, reaching over 18% top-1 accuracy on MassSpecGym and tripling prior bests on NPLIB1 via large unlabeled training and inference-time fragmentation refinement with log-linear compute scaling.
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Generative Chemical Language Models for Energetic Materials Discovery
Generative chemical language models pretrained on general chemical data and fine-tuned on energetic materials datasets enable accelerated discovery of synthetically accessible high-performance compounds.