Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me...Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.展开更多
The authors provided a simple method for calculating Wiener numbers of molecular graphs with symmetry in 1997.This paper intends to further improve on it and simplifies the calculation of the Wiener numbers of the mol...The authors provided a simple method for calculating Wiener numbers of molecular graphs with symmetry in 1997.This paper intends to further improve on it and simplifies the calculation of the Wiener numbers of the molecular graphs.展开更多
Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural ...Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.展开更多
In theoretical chemistry, the geometric-arithmetic indices were introduced to measure the stability of alkanes and the strain energy of cycloalkanes. In this note, we report the general third geometric-arithmetic inde...In theoretical chemistry, the geometric-arithmetic indices were introduced to measure the stability of alkanes and the strain energy of cycloalkanes. In this note, we report the general third geometric-arithmetic index of unilateral polyomino chain and unilateral hexagonal chain. Also, the third geometric-arithmetic index of these chemical structures are presented.展开更多
Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of po...Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory.Four key structural descriptors,flexibility,side chain occupancy length,polarity,and hydrogen bonding capacity,were extracted and used as inputs for ML models:Extra Trees(ET),Random Forest(RF),Gaussian Process Regression(GPR),and Gradient Boosting(GB).Among these,ET and GPR achieved the highest predictive performance,with R2 values of 0.97,and mean absolute errors(MAE)of approximately 7–7.5 K.The use of these extracted features significantly improved the prediction accuracy compared to previous studies.Feature importance analysis revealed that flexibility had the strongest influence on Tg,followed by side-chain occupancy length,hydrogen bonding,and polarity.This work demonstrates the potential of data-driven approaches in polymer science,providing a fast and reliable method for Tg prediction that does not require experimental inputs.展开更多
Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological in...Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological index is . In this paper, the Schultz, Modified Schultz polynomials and their topological indices of Jahangir graphs J<sub>2,m</sub> for all integer number m ≥ 3 are calculated.展开更多
抗癌药物组合的协同性筛选对于临床治疗具有重要意义,但随着药物组合数量的爆炸式增长,传统检测方法存在耗时长、成本高等问题,难以有效发现新的协同药物组合。针对上述问题,提出一种基于图注意力网络的抗癌药物组合协同性预测模型(mult...抗癌药物组合的协同性筛选对于临床治疗具有重要意义,但随着药物组合数量的爆炸式增长,传统检测方法存在耗时长、成本高等问题,难以有效发现新的协同药物组合。针对上述问题,提出一种基于图注意力网络的抗癌药物组合协同性预测模型(multi-scale feature fusion model based on graph attention network for anticancer synergistic drug combination prediction,MFGSynergy)来辅助抗癌药物组合筛选。首先,该模型将药物简化分子线性输入规范(simplified molecular input line entry system,SMILES)编码为分子图及分子指纹数据,并对癌细胞系数据进行预处理;然后,通过图注意力网络(graph attention network,GAT)和多层感知机(multilayer perceptron,MLP)对药物数据及癌细胞系数据进行特征提取,并将提取到的多种药物特征和癌细胞系特征进行特征融合用于预测抗癌药物组合的协同性;最后,基于公开数据集将MFGSynergy与Deep DDS、DeepSynergy及6种机器学习方法进行对比实验,实验结果表明,MFGSynergy在五折交叉验证上的ROC曲线下的面积(receiver operating characteristic area under the curve,ROC AUC)、PR曲线下的面积(area under the precision-recall curve,AUPR)、准确性(accuracy,ACC)、精准度(precision,PREC)、真阳性率(true positive rate,TPR)和F1分别达到了0.94、0.94、0.86、0.87、0.86、0.86,均高于其他对比模型,这说明MFGSynergy的预测性能优于其他对比模型。此外,独立测试实验表明,对于未知的药物组合,MFGSynergy仍具有良好的预测性能,这证明模型具有良好的泛化性。展开更多
基金supported by Macao Science and Technology Development Fund,Macao SAR,China(Grant No.:0043/2023/AFJ)the National Natural Science Foundation of China(Grant No.:22173038)Macao Polytechnic University,Macao SAR,China(Grant No.:RP/FCA-01/2022).
文摘Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
文摘The authors provided a simple method for calculating Wiener numbers of molecular graphs with symmetry in 1997.This paper intends to further improve on it and simplifies the calculation of the Wiener numbers of the molecular graphs.
文摘Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.
文摘In theoretical chemistry, the geometric-arithmetic indices were introduced to measure the stability of alkanes and the strain energy of cycloalkanes. In this note, we report the general third geometric-arithmetic index of unilateral polyomino chain and unilateral hexagonal chain. Also, the third geometric-arithmetic index of these chemical structures are presented.
文摘Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory.Four key structural descriptors,flexibility,side chain occupancy length,polarity,and hydrogen bonding capacity,were extracted and used as inputs for ML models:Extra Trees(ET),Random Forest(RF),Gaussian Process Regression(GPR),and Gradient Boosting(GB).Among these,ET and GPR achieved the highest predictive performance,with R2 values of 0.97,and mean absolute errors(MAE)of approximately 7–7.5 K.The use of these extracted features significantly improved the prediction accuracy compared to previous studies.Feature importance analysis revealed that flexibility had the strongest influence on Tg,followed by side-chain occupancy length,hydrogen bonding,and polarity.This work demonstrates the potential of data-driven approaches in polymer science,providing a fast and reliable method for Tg prediction that does not require experimental inputs.
文摘Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological index is . In this paper, the Schultz, Modified Schultz polynomials and their topological indices of Jahangir graphs J<sub>2,m</sub> for all integer number m ≥ 3 are calculated.
文摘抗癌药物组合的协同性筛选对于临床治疗具有重要意义,但随着药物组合数量的爆炸式增长,传统检测方法存在耗时长、成本高等问题,难以有效发现新的协同药物组合。针对上述问题,提出一种基于图注意力网络的抗癌药物组合协同性预测模型(multi-scale feature fusion model based on graph attention network for anticancer synergistic drug combination prediction,MFGSynergy)来辅助抗癌药物组合筛选。首先,该模型将药物简化分子线性输入规范(simplified molecular input line entry system,SMILES)编码为分子图及分子指纹数据,并对癌细胞系数据进行预处理;然后,通过图注意力网络(graph attention network,GAT)和多层感知机(multilayer perceptron,MLP)对药物数据及癌细胞系数据进行特征提取,并将提取到的多种药物特征和癌细胞系特征进行特征融合用于预测抗癌药物组合的协同性;最后,基于公开数据集将MFGSynergy与Deep DDS、DeepSynergy及6种机器学习方法进行对比实验,实验结果表明,MFGSynergy在五折交叉验证上的ROC曲线下的面积(receiver operating characteristic area under the curve,ROC AUC)、PR曲线下的面积(area under the precision-recall curve,AUPR)、准确性(accuracy,ACC)、精准度(precision,PREC)、真阳性率(true positive rate,TPR)和F1分别达到了0.94、0.94、0.86、0.87、0.86、0.86,均高于其他对比模型,这说明MFGSynergy的预测性能优于其他对比模型。此外,独立测试实验表明,对于未知的药物组合,MFGSynergy仍具有良好的预测性能,这证明模型具有良好的泛化性。
