Pheng‐Ann Heng

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

SubcellPert benchmarks subcellular perturbation prediction using imaging data, showing Gaussian transport achieves low spatial error in out-of-distribution datasets.

This paper introduces PROTAC-STAN, a deep learning framework for interpretable PROTAC degradation prediction, achieving improved accuracy and model interpretability.

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

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.