Amitesh Badkul

PK-MUSE improves pharmacokinetics prediction by combining mechanistic models with neural corrections, achieving better performance across unseen chemical structures.

This paper evaluates five AI frameworks on real scientific tasks, finding they excel in hypothesis generation and reporting but struggle with depth and reliability in real-world research.

DrugPTM-Bench provides a large-scale phosphoproteomics dataset for modeling drug-induced PTM regulation, highlighting challenges in imbalanced classification and enabling multi-task predictive tasks.

This paper evaluates AI frameworks' ability to autonomously conduct scientific research, finding they cannot complete full research cycles but may assist with specific subtasks.

The paper introduces eMOSAIC, a method for uncertainty quantification in multi-target ligand binding affinity predictions, showing it outperforms existing methods in out-of-distribution settings.

PortalCG predicts chemical-protein interactions for dark proteins using a deep learning framework that integrates binding site information and meta-learning, outperforming existing methods in out-of-distribution scenarios.