Yang Liu

Model Agnostic Semi-Supervised Meta-Learning Elucidates Understudied Out-of-distribution Molecular Interactions

The paper introduces MMAPLE, a semi-supervised meta-learning framework that improves prediction of out-of-distribution molecular interactions by leveraging unlabeled data, achieving significant improvements over baselines.

Exploration of Chemical Space with Partial Labeled Noisy Student Self-Training for Improving Deep Learning: Application to Drug Metabolism

The paper introduces PLANS, a self-training method that improves deep learning models for drug discovery by using unlabeled and partially labeled data, achieving significant performance gains in predicting Cytochrome P450 binding activity.