Qinze Yu

Trimole-Hybrid improves ADMET prediction by combining multiple molecular representations and task-specific models, achieving top performance on 22 benchmarks.

This paper introduces MultiAMP, a framework for predicting and designing antimicrobial peptides by integrating sequence and structural information, achieving state-of-the-art performance and discovering novel peptides.

VIRALpre improves virus identification by combining genomic foundation model embeddings with K-mer features, achieving higher accuracy and better performance on short contigs and cross-dataset tests.

A BERT-style model pretrained on 23.7 million unlabelled ncRNA sequences gives embeddings that improve secondary structure, contact, distance-map and some function predictions over task-specific and MSA-based baselines.

This study introduces HMD-AMP, a deep learning framework for multi-label classification of antimicrobial peptides, improving performance on minor classes and handling multiple targets.