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

2026-02-08

AI Summary

This work proposes a coarse-grained, diffusion-free multimodal framework for drug–protein binding affinity prediction, addressing the high computational cost of existing all-atom diffusion models that hinders large-scale screening. By representing binding pockets using only protein Cβ atoms and ligand heavy atoms, the method integrates COATI-3 molecular encodings with ESM-2 protein embeddings and incorporates diffusion-free conformational optimization, affinity likelihood prediction, calibratable uncertainty estimation, and continual learning. The approach achieves conformation generation accuracy comparable to diffusion-based models while improving Pearson correlation coefficients for affinity prediction by approximately 20%. Furthermore, it demonstrates a six-fold increase in molecular optimization efficiency over greedy strategies, offering a compelling balance between computational efficiency and predictive reliability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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$20\%. Current deep learning approaches to structure-based drug design rely on expensive all-atom diffusion to generate 3D coordinates, creating inference bottlenecks that render large-scale compound screening computationally intractable. We challenge this paradigm with a critical hypothesis: full all-atom resolution is unnecessary for accurate small molecule pose and binding affinity prediction. TerraBind tests this hypothesis through a coarse pocket-level representation (protein C$_\beta$ 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.

Problem

• binding affinity prediction
• structure-based drug design
• computational bottleneck
• large-scale compound screening
• all-atom diffusion

Innovation

• coarse-grained representation
• diffusion-free pose optimization
• binding affinity prediction
• multimodal protein-ligand modeling
• uncertainty-aware drug discovery