Author

R. Gorantla

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Open access Oct 2026

GRASP: Graph Representation Learning with Assay Supervision for Molecular Properties

Molecular structures are abundant, but measured properties are scarce and scattered across assays that each cover different compounds. Most molecular pretraining pipelines use only the structures, moving directly from self-supervision to endpoint-specific fine-tuning and leaving large, sparsely labelled bioactivity dat...

S. P. Srivastava, R. Gorantla, Sharath Krishna Chundru et al. · bioRxiv · 0 citations

#protein folding Open access Sep 2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

R. Gorantla, C. Schleberger, F. Sesterhenn · bioRxiv · 0 citations