用深度学习预测制冷剂全球变暖潜势，提升环保替代品研发效率。
Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling
- 融合主成分分析与分位数变换降维，提升高维数据建模稳定性。
- 基于RDKit特征的模型RMSE达481.9，决定系数R2达0.918，精度高。
- 识别出分子量、疏水性等关键结构特征，指导绿色制冷剂设计。
开发环境可持续制冷剂对缓解人为温室气体导致的全球变暖至关重要。本研究提出一种预测单组分制冷剂100年全球变暖潜势（GWP 100）的建模框架，采用全连接神经网络在Multi-Sigma平台上实现。使用RDKit、Mordred和alvaDesc提取分子描述符以捕捉多种化学特征。基于RDKit的模型表现最佳，均方根误差（RMSE）为481.9，决定系数（R2）达0.918，展现出优异的预测准确性和泛化能力。通过主成分分析（PCA）和分位数变换处理高维且偏斜的数据集，增强了模型稳定性与性能。因子分析识别出分子量、脂溶性及腈基、烯丙基氧化物等功能团是影响GWP的关键因素。该框架结合RDKit描述符与Multi-Sigma平台的PCA、分位数变换与神经网络，为低GWP制冷剂的快速虚拟筛选提供可扩展解决方案，有望加速环保替代品发现，直接助力气候减缓目标。
原文摘要 · Abstract (English)
Developing environmentally sustainable refrigerants is critical for mitigating the impact of anthropogenic greenhouse gases on global warming. This study presents a predictive modeling framework to estimate the 100-year global warming potential (GWP 100) of single-component refrigerants using a fully connected neural network implemented on the Multi-Sigma platform. Molecular descriptors from RDKit, Mordred, and alvaDesc were utilized to capture various chemical features. The RDKit-based model achieved the best performance, with a Root Mean Square Error (RMSE) of 481.9 and an R2 score of 0.918, demonstrating superior predictive accuracy and generalizability. Dimensionality reduction through Principal Component Analysis (PCA) and quantile transformation were applied to address the high-dimensional and skewed nature of the dataset,enhancing model stability and performance. Factor analysis identified vital molecular features, including molecular weight, lipophilicity, and functional groups, such as nitriles and allylic oxides, as significant contributors to GWP values. These insights provide actionable guidance for designing environmentally sustainable refrigerants. Integrating RDKit descriptors with Multi-Sigma's framework, which includes PCA, quantile transformation, and neural networks, provides a scalable solution for the rapid virtual screening of low-GWP refrigerants. This approach can potentially accelerate the identification of eco-friendly alternatives, directly contributing to climate mitigation by enabling the design of next-generation refrigerants aligned with global sustainability objectives.
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