Composite machine learning strategy for natural products taxonomical classification and structural insights†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-09-23 DOI:10.1039/D4DD00155A
Qisong Xu, Alan K. X. Tan, Liangfeng Guo, Yee Hwee Lim, Dillon W. P. Tay and Shi Jun Ang
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Abstract

Taxonomical classification of natural products (NPs) can assist in genomic and phylogenetic analysis of source organisms and facilitate streamlining of bioprospecting efforts. Here, a composite machine learning strategy marrying graph convolutional neural networks (GCNNs) and eXteme Gradient boosting (XGB) is proposed and validated for taxonomical classification of NPs in five kingdoms (Animalia, Bacteria, Chromista, Fungi, and Plantae). Our composite model, trained on 133 092 NPs from the LOTUS database, achieved five-fold cross-validated classification accuracy of 97.4%. When employed to classify out-of-sample NPs from the NP Atlas database, accuracies of 82.8% for bacteria and 86.6% for fungi were obtained. Dimensionality-reduced representations of the molecular embeddings from our composite model revealed distinct clusters of NPs that suggest a basis for enhanced classification performance. The top critical substructures from the NPs of each kingdom were also identified and compared to provide insights on structure–taxonomy relationships. Overall, this study showcases the potential of composite machine learning models for robust taxonomical classification of NPs, which can streamline discovery of NPs.

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天然产品分类和结构洞察的复合机器学习策略†
对天然产物(NPs)进行分类有助于对源生物进行基因组和系统发育分析,并有助于简化生物勘探工作。本文提出了一种将图卷积神经网络(GCNN)和梯度提升技术(XGB)结合起来的复合机器学习策略,并对五界(动物界、细菌界、染色体界、真菌界和植物界)的天然产物分类进行了验证。我们的复合模型是在 LOTUS 数据库的 133 092 个 NPs 上训练出来的,经过五倍交叉验证,分类准确率达到 97.4%。在对 NP Atlas 数据库中的样本外 NP 进行分类时,细菌和真菌的准确率分别为 82.8% 和 86.6%。我们的复合模型中分子嵌入的降维表示法揭示了NPs的独特群集,为提高分类性能提供了基础。此外,我们还识别并比较了每个生物界 NPs 中最重要的子结构,从而为结构-分类关系提供了深入的见解。总之,这项研究展示了复合机器学习模型在对 NPs 进行稳健分类方面的潜力,它可以简化 NPs 的发现过程。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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