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Found 5 papers, 3 papers with code
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning
Protein generative models have shown remarkable promise in protein design but still face limitations in success rate, due to the scarcity of high-quality protein datasets for supervised pretraining.
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
To address these challenges, we introduce PepHAR, a hot-spot-driven autoregressive generative model for designing peptides targeting specific proteins.
FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames
Despite the striking success of general protein folding models such as AlphaFold2(AF2, Jumper et al. (2021)), the accurate computational modeling of antibody-antigen complexes remains a challenging task.
Full-Atom Peptide Design based on Multi-modal Flow Matching
Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery.
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning.
