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Found 5 papers, 4 papers with code
AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language ESM2 model demonstrates superior performance across the evaluations.
PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding
This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task.
PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids
In recent years, the scientific community has become increasingly interested on peptides with non-canonical amino acids due to their superior stability and resistance to proteolytic degradation.
Optimization of binding affinities in chemical space with generative pretrained transformer and deep reinforcement learning
The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal.
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real world problems.
