TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations

Matteo Rossi; Ryan Pederson; Miles Wang-Henderson; Ben Kaufman; Edward C. Williams; Carl Underkoffler; Owen Lewis Howell; Adrian Layer; Stephan Thaler; Narbe Mardirossian; John Anthony Parkhill

arXiv · 2602.07735

TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations

Matteo Rossi·Ryan Pederson·Miles Wang-Henderson·Ben Kaufman·Edward C. Williams·Carl Underkoffler·Owen Lewis Howell·Adrian Layer·Stephan Thaler·Narbe Mardirossian·John Anthony Parkhill

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Abstract

We present TerraBind, a foundation model for protein-ligand structure and binding affinity prediction that achieves 26-fold faster inference than state-of-the-art methods while improving affinity prediction accuracy by $\sim$20\%. Current deep learning approaches to structure-based drug design rely on expensive all-atom diffusion to generate 3D coordinates, creating inference bottlenecks that render large-scale compound screening computationally intractable. We challenge this paradigm with a critical hypothesis: full all-atom resolution is unnecessary for accurate small molecule pose and binding affinity prediction. TerraBind tests this hypothesis through a coarse pocket-level representation (protein C$_β$ atoms and ligand heavy atoms only) within a multimodal architecture combining COATI-3 molecular encodings and ESM-2 protein embeddings that learns rich structural representations, which are used in a diffusion-free optimization module for pose generation and a binding affinity likelihood prediction module. On structure prediction benchmarks (FoldBench, PoseBusters, Runs N' Poses), TerraBind matches diffusion-based baselines in ligand pose accuracy. Crucially, TerraBind outperforms Boltz-2 by $\sim$20\% in Pearson correlation for binding affinity prediction on both a public benchmark (CASP16) and a diverse proprietary dataset (18 biochemical/cell assays). We show that the affinity prediction module also provides well-calibrated affinity uncertainty estimates, addressing a critical gap in reliable compound prioritization for drug discovery. Furthermore, this module enables a continual learning framework and a hedged batch selection strategy that, in simulated drug discovery cycles, achieves 6$\times$ greater affinity improvement of selected molecules over greedy-based approaches.

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BibTeX RIS

Matteo Rossi, Ryan Pederson, Miles Wang-Henderson, Ben Kaufman, Edward C. Williams, Carl Underkoffler, Owen Lewis Howell, Adrian Layer, Stephan Thaler, Narbe Mardirossian, John Anthony Parkhill. 2026-02-08. TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations. https://arxiv.org/abs/2602.07735

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