Hansaim Lim

This paper introduces DISAE, a deep learning framework that predicts ligands for unannotated proteins and applies it to assign ligands to human orphan GPCRs.

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.

This paper introduces DISAE, a protein pre-training method for deorphanization, which improves generalizability and outperforms existing methods in predicting small molecule binding to orphan proteins.

The paper develops a Bayesian method to improve gene expression signal detection in LINCS L1000 data, showing better reliability and informativeness than existing approaches.

The paper develops a multi-target screening platform to discover dual-action drugs for cancer therapy, identifying levosimendan as a potential candidate that reduces heart failure risk and inhibits cancer growth.

The paper develops a multi-rank, multi-layered recommender system, ANTENNA, to predict drug-gene-disease associations and repurposes diazoxide as a potential anti-cancer therapy for triple-negative breast cancer.