Defines Conditional Distribution Matching (CDM) as finding inputs whose induced conditional distributions match a target distribution and proposes the MLGD-F inference-time algorithm using pretrained diffusion models to solve it without retraining.
Robust deep learning–based protein sequence design using proteinmpnn.Science, 378(6615):49–56
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 5representative citing papers
VibeProteinBench is a new benchmark evaluating LLMs on open-ended language-interfaced protein design across recognition, engineering, and generation, with no model showing strong performance in all areas.
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.
SGRPO expands the utility-diversity Pareto frontier in biomolecular design by using supergroup sampling and leave-one-out diversity rewards combined with utility signals.
MolClaw deploys a hierarchical skill system (tool, workflow, and discipline levels) to achieve state-of-the-art results on MolBench tasks requiring 8 to 50+ sequential tool calls in drug discovery.
citing papers explorer
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Inverse Design for Conditional Distribution Matching
Defines Conditional Distribution Matching (CDM) as finding inputs whose induced conditional distributions match a target distribution and proposes the MLGD-F inference-time algorithm using pretrained diffusion models to solve it without retraining.
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VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design
VibeProteinBench is a new benchmark evaluating LLMs on open-ended language-interfaced protein design across recognition, engineering, and generation, with no model showing strong performance in all areas.
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Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.
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Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization
SGRPO expands the utility-diversity Pareto frontier in biomolecular design by using supergroup sampling and leave-one-out diversity rewards combined with utility signals.
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MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization
MolClaw deploys a hierarchical skill system (tool, workflow, and discipline levels) to achieve state-of-the-art results on MolBench tasks requiring 8 to 50+ sequential tool calls in drug discovery.