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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.
An FEA surrogate model with Boundary Oriented Graph Embedding approach
In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems.
Heuristic Black-box Adversarial Attacks on Video Recognition Models
To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions.
