Nagaraj M. Kulkarni

PATHNet improves protein-ligand interaction prediction by combining physics-inspired modeling with neural networks, achieving high accuracy across multiple targets.

DMPKformer predicts ADMET properties using a multimodal deep learning framework that integrates molecular representations, achieving competitive performance and interpretability.

This paper introduces MOLDE, an in-silico method for designing molecular glues to target undruggable proteins by leveraging known ternary complexes.

This paper introduces a computational method for in-silico PROTAC design and evaluation, demonstrating its ability to predict PROTAC binding modes and thermodynamics for known and novel target systems.

This paper establishes an in-silico rationale for molecular glue design by analyzing a known case and discusses its application in designing novel molecular glues using AI techniques.

This paper presents an in-silico approach for PROTAC design that uses AI and molecular dynamics to predict ternary complex stability and quantify PROTAC performance metrics.