Guangyong Chen

The paper develops DeepDelivery, an AI platform that identifies human proteins for mRNA delivery, validating 80% of top candidates for efficient, non-inflammatory delivery.

CatESO optimizes enzyme sequences to improve catalytic turnover by integrating kcat prediction with differentiable design, achieving higher predicted activity and structural confidence than existing methods.

BRIDGE generates virtual spatial transcriptomes from histology images, improving cancer diagnosis accuracy in few-shot settings and outperforming existing models.

This paper introduces a deep reinforcement learning framework for designing new antibiotics, producing potent peptides with broad-spectrum activity against bacteria.

The paper presents an LLM-based workflow for extracting enzyme kinetic data from literature, achieving high accuracy and efficiency.

siRNADesign uses a Graph Neural Network to predict siRNA efficacy by analyzing RNA sequences and interactions, achieving state-of-the-art performance in both computational and experimental settings.

InstructPLM aligns a protein language model with structure instructions to generate diverse proteins, improving performance over existing models in sequence recovery and activity.

This paper introduces an iterative sequence refinement pipeline for inverse protein folding that improves sequence recovery and editing activity in transposase variants.