End-to-End Reverse Screening Identifies Protein Targets of Small Molecules Using HelixFold3

2026-01-20

AI Summary

Traditional reverse screening approaches suffer from error accumulation due to the decoupling of structural modeling, binding pocket identification, and docking scoring, often leading to inaccurate target identification for small molecules. This work proposes the first end-to-end reverse screening framework that leverages HelixFold3 to unify protein structure prediction and small-molecule docking, enabling direct identification of potential targets from a protein library. By integrating high-accuracy macromolecular structure prediction into reverse screening for the first time, the method circumvents the pitfalls of multi-step pipelines. Evaluated on a test set of approximately one hundred small molecules, it substantially outperforms conventional approaches in target ranking accuracy, binding site localization precision, and complex structural fidelity.

Technology Category

• Machine Learning: Structured Learning
• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Web

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Identifying protein targets for small molecules, or reverse screening, is essential for understanding drug action, guiding compound repurposing, predicting off-target effects, and elucidating the molecular mechanisms of bioactive compounds. Despite its critical role, reverse screening remains challenging because accurately capturing interactions between a small molecule and structurally diverse proteins is inherently complex, and conventional step-wise workflows often propagate errors across decoupled steps such as target structure modeling, pocket identification, docking, and scoring. Here, we present an end-to-end reverse screening strategy leveraging HelixFold3, a high-accuracy biomolecular structure prediction model akin to AlphaFold3, which simultaneously models the folding of proteins from a protein library and the docking of small-molecule ligands within a unified framework. We validate this approach on a diverse and representative set of approximately one hundred small molecules. Compared with conventional reverse docking, our method improves screening accuracy and demonstrates enhanced structural fidelity, binding-site precision, and target prioritization. By systematically linking small molecules to their protein targets, this framework establishes a scalable and straightforward platform for dissecting molecular mechanisms, exploring off-target interactions, and supporting rational drug discovery.

Problem

• reverse screening
• protein targets
• small molecules
• off-target effects
• molecular mechanisms

Innovation

• end-to-end reverse screening
• HelixFold3
• structure prediction
• molecular docking
• target identification