揭示药物在二维Graphsene上的吸附与电子相互作用：来自DFT和机器学习方法的见解
Unveiling the Adsorption and Electronic Interactions of Drugs on 2D Graphsene: Insights from DFT and Machine Learning Approach
AI总结：
本文结合DFT与机器学习，研究药物在二维多孔石墨烯同素异形体Graphsene上的吸附与电子耦合，利用67种药物数据训练模型并以0.075 eV误差预测候选药物，为数据驱动的纳米药物递送系统设计提供高效筛选方法。
摘要：
高效识别用于纳米材料递送系统的有前景候选药物，对于推进下一代治疗手段至关重要。在本工作中，我们提出了一种将密度泛函理论（DFT）与机器学习（ML）相结合的协同框架，用于探索药物在一种新型二维石墨烯同素异形体——名为Graphsene（GrS）——上的吸附行为和电子相互作用。Graphsene以其多孔环拓扑结构和大比表面积为特征，为高效吸附以及与药物分子的强电子耦合提供了优异平台。我们构建了一个包含67种药物吸附在多种二维基底上的数据集，用于训练ML模型，随后将该模型应用于基于分子尺寸和吸附能标准预测适合GrS的候选药物（数据库链接在后续章节中提供）。该ML模型表现出稳健的预测精度，经DFT验证后平均绝对误差为0.075 eV，但其对初始化的敏感性表明需要更大规模且更多样化的数据集。基于DFT的分析，包括吸附能量学、投影态密度（PDOS）和Bader电荷计算，揭示了药物分子与GrS表面之间存在显著的电荷转移和电子耦合，阐明了药物-基底相互作用的基本本质。研究表明，DFT-ML集成策略为筛选和理解药物-纳米材料相互作用提供了一种快速、经济高效的方法，为数据驱动的先进纳米材料药物递送系统设计铺平了道路。
英文摘要：
Efficient identification of promising drug candidates for nanomaterial-based delivery systems is essential for advancing next-generation therapeutics. In this work, we present a synergistic framework combining density functional theory (DFT) and machine learning (ML) to explore the adsorption behavior and electronic interactions of drugs on a novel 2D graphene allotrope, termed Graphsene (GrS). Graphsene, characterized by its porous ring topology and large surface area, offers an excellent platform for efficient adsorption and strong electronic coupling with drug molecules. A dataset comprising 67 drugs adsorbed on various 2D substrates was employed to train the ML model, which was subsequently applied to predict suitable drug candidates for GrS based on molecular size and adsorption energy criteria (database link provided in a later section). The ML model exhibited robust predictive accuracy, achieving a mean absolute error of 0.075 eV upon DFT validation, though its sensitivity to initialization highlighted the need for larger and more diverse datasets. DFT-based analyses, including adsorption energetics, projected density of states (PDOS), and Bader charge calculations, revealed pronounced charge transfer and electronic coupling between the drug molecules and the GrS surface, elucidating the fundamental nature of drug-substrate interactions. The study reveals that the integrated DFT-ML strategy offers a rapid, cost-efficient approach for screening and understanding drug-nanomaterial interactions, paving the way for data-driven design of advanced nanomaterial-enabled drug delivery systems.
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