Predictive modeling of antibacterial activity of ionic liquids by machine learning methods

Makarov, Dmitriy M.; Fadeeva, Yu. A.; Сафонова, Е. А.; Shmukler, L. E.

doi:10.1016/j.compbiolchem.2022.107775

Cited by 5 publications

(5 citation statements)

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“…Graph attention allocates a learnable weight for every edge while performing feature aggregation on nodes. In this study, each GAT layer has eight attention heads; the hidden representation size in the output MLP predictor is 128, 30 atom features, a batch size of 128, and the channel width per attention head for GAT layers is (8,8).…”

Section: Modelsmentioning

confidence: 99%

“…Designing task-specific ILs with desired MPs is challenging because a wide range of factors, including hydrogen bonding, van der Waals interaction, and charge distribution, can affect the MPs . Because trillion types of ILs can be synthesized in a lab, finding the appropriate ILs via experimental screening is costly and time-consuming. , Quantitative structure–property relationship (QSPR) studies − have been used to accurately forecast the MPs of ILs. The goal of QSPR techniques is to create mathematical representations of numerical properties based on the structural details of chemical substances .…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML) techniques show promise for accurately and effectively predicting the properties of chemical compounds. ,− In different fields, ML methods , are as accurate as traditional simulation techniques such as MD but require less computing power. , Molecular descriptor–based ML (DBML) models have been used to forecast the MPs of ILs. , Molecular descriptors are the numerical values that feature the molecular structures of the ILs. One of the earlier descriptors used group contribution, , which split molecules into fragments; the MP of an IL is calculated by summing the contribution of each fragment.…”

Section: Introductionmentioning

confidence: 99%

“… 4 Because trillion types of ILs can be synthesized in a lab, finding the appropriate ILs via experimental screening is costly and time-consuming. 8 , 9 Quantitative structure–property relationship (QSPR) studies 10 − 12 have been used to accurately forecast the MPs of ILs. The goal of QSPR techniques is to create mathematical representations of numerical properties based on the structural details of chemical substances.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML) techniques show promise for accurately and effectively predicting the properties of chemical compounds. 9 , 16 − 20 In different fields, ML methods 8 , 21 are as accurate as traditional simulation techniques such as MD but require less computing power. 14 , 22 Molecular descriptor–based ML (DBML) models have been used to forecast the MPs of ILs.…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks

Feng,

Qin,

Zhang

et al. 2024

ACS Omega

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Ionic liquids (ILs) have wide and promising applications in fields such as chemical engineering, energy, and the environment. However, the melting points (MPs) of ILs are one of the most crucial properties affecting their applications. The MPs of ILs are affected by various factors, and tuning these in a laboratory is time-consuming and costly. Therefore, an accurate and efficient method is required to predict the desired MPs in the design of novel targeted ILs. In this study, three descriptor-based machine learning (DBML) models and eight graph neural network (GNN) models were proposed to predict the MPs of ILs. Fingerprints and molecular graphs were used to represent molecules for the DBML and GNNs, respectively. The GNN models demonstrated performance superior to that of the DBML models. Among all of the examined models, the graph convolutional model exhibited the best performance with high accuracy (root-mean-squared error = 37.06, mean absolute error = 28.79, and correlation coefficient = 0.76). Benefiting from molecular graph representation, we built a GNN-based interpretable model to reveal the atomistic contribution to the MPs of ILs using a data-driven procedure. According to our interpretable model, amino groups, S + , N + , and P + would increase the MPs of ILs, while the negatively charged halogen atoms, S − , and N − would decrease the MPs of ILs. The results of this study provide new insight into the rapid screening and synthesis of targeted ILs with appropriate MPs.

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Section: Modelsmentioning

confidence: 99%