Satya Pratik Srivastava

This paper benchmarks a Gaussian process active learning framework for antibody-antigen binding affinity prediction, showing significant improvements over random selection.

This study evaluates if language models improve combinatorial molecular search for ionizable lipids compared to classical methods under budget constraints, finding no significant improvement.

CLARA uses a language model to make strategy decisions in compound library active learning, outperforming random sampling across multiple protein targets.

The paper evaluates whether molecular models can causally represent chemical variables by comparing intervention effects with random controls and pretraining advantages.

GRASP is a graph Transformer that learns molecular structure from ZINC20 data and adapts to ChEMBL bioactivity, achieving better performance than existing models on ADMET prediction tasks.

AbAffinity predicts antibody-antigen affinity using a chain-aware sequence model, achieving high correlation on benchmark datasets and maintaining performance without structural data.

BALM-PPI predicts protein-protein binding affinity from sequence using a fine-tuned protein language model, achieving accuracy comparable to structure-based methods and providing residue-level explanations.

This paper develops an explainable active learning framework for ligand binding affinity prediction, showing that method choices affect outcomes but dataset characteristics set performance limits.