RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
Atomic context-conditioned protein sequence design using LigandMPNN.Nature Methods, 22(4):717–723, 2025
3 Pith papers cite this work, alongside 177 external citations. Polarity classification is still indexing.
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A ~3.3M-parameter dense PairMixer inverse folder trails LigandMPNN on interface recovery while propagating ligand signal to distal residues where local graphs lose it.
Emyx, a compact flow matching model with EDM reparametrization, outperforms larger protein generators on enzyme design benchmarks with substantially lower training compute.
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RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
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UMA-Inverse: Ligand-Conditioned Protein Inverse Folding with a Distogram-Supervised Dense Pair Encoder
A ~3.3M-parameter dense PairMixer inverse folder trails LigandMPNN on interface recovery while propagating ligand signal to distal residues where local graphs lose it.
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Emyx: Fast and efficient all-atom protein generation
Emyx, a compact flow matching model with EDM reparametrization, outperforms larger protein generators on enzyme design benchmarks with substantially lower training compute.