Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset

<b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.<b>用于预测和解释小分子配体与RNA结合的结构相互作用指纹及机器学习：一个基准数据集</b> 核糖核酸（RNA）在生物体中发挥着至关重要的作用，参与维持细胞正常功能所需的关键生命过程。部分RNA分子（如细菌核糖体、前体信使RNA）可作为小分子药物的作用靶点，而细菌核糖开关、病毒RNA基序等其他RNA分子则被视为潜在的治疗靶点。因此，随着具有新功能的RNA不断被发现，开发靶向RNA的化合物以及分析RNA与小分子相互作用的方法的需求日益增长。我们近期开发了fingeRNAt——一款用于检测核酸与不同类型配体形成的复合物内部非共价键的软件。该软件可检测多种非共价相互作用，包括氢键、卤键、离子相互作用、π相互作用、无机离子介导相互作用以及水介导相互作用、亲脂相互作用，并将这些相互作用编码为便于计算的结构相互作用指纹（Structural Interaction Fingerprint，SIFt）。本研究展示了将SIFt与机器学习方法相结合，用于预测小分子与RNA靶点结合情况的应用。研究表明，基于SIFt的模型在虚拟筛选任务中优于经典的通用评分函数。我们还探讨了可解释人工智能（Explainable Artificial Intelligence）在结合预测模型分析中的辅助作用，阐明模型的决策过程，并解析分子识别机制。