Learning to Align Molecules and Proteins: A Geometry-Aware Approach to Binding Affinity

2025-09-24

AI Summary

Accurate and generalizable drug–target binding affinity prediction remains challenging, as existing deep learning models often rely on simplistic feature concatenation without geometric constraints, limiting extrapolation beyond training chemical spaces. To address this, we propose a geometry-aware molecular alignment framework that jointly models ligand–protein conditional dependencies via Feature-wise Linear Modulation (FiLM) and enforces metric consistency in the embedding space through an RBF-based regression head coupled with a triplet distance loss. This lightweight architecture explicitly incorporates structural priors into representation learning. Evaluated on the Therapeutics Data Commons DTI-DG benchmark, our method achieves state-of-the-art performance. Ablation studies confirm the efficacy of each component, while cross-domain experiments demonstrate substantial improvements in out-of-distribution generalization and model interpretability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate prediction of drug-target binding affinity can accelerate drug discovery by prioritizing promising compounds before costly wet-lab screening. While deep learning has advanced this task, most models fuse ligand and protein representations via simple concatenation and lack explicit geometric regularization, resulting in poor generalization across chemical space and time. We introduce FIRM-DTI, a lightweight framework that conditions molecular embeddings on protein embeddings through a feature-wise linear modulation (FiLM) layer and enforces metric structure with a triplet loss. An RBF regression head operating on embedding distances yields smooth, interpretable affinity predictions. Despite its modest size, FIRM-DTI achieves state-of-the-art performance on the Therapeutics Data Commons DTI-DG benchmark, as demonstrated by an extensive ablation study and out-of-domain evaluation. Our results underscore the value of conditioning and metric learning for robust drug-target affinity prediction.

Problem

• Predicting drug-target binding affinity to accelerate drug discovery
• Improving generalization across chemical space and time
• Incorporating geometric regularization for robust affinity prediction

Innovation

• Feature-wise linear modulation layers condition molecular embeddings on proteins
• Triplet loss enforces metric structure for geometric regularization
• RBF regression on embedding distances yields interpretable affinity predictions