Machine learning to predict the specific optical rotations of chiral fluorinated molecules

Chen, Mengyao; Wu, Ting; Xiao, Kaixia; Zhao, Tanfeng; Zhou, Yanmei; Zhang, Qingyou; Aires-de-Sousa, João

doi:10.1016/j.saa.2019.117289

Cited by 6 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quantitative structure-property/activity relationship (QSPR/QSAR) research is a widely used alternative to traditional experiments, and also have been applied to some studies on the prediction of the property of ILs. [6][7][8][9][10][11][12] Such as the imidazole ionic liquids were analyzed with QSPR techniques and melting temperature were estimated with clustering methods; [13] the melting point of 126 structurally diverse pyridinium bromides was predicted by QSPR models with the CODESSA program; [14] the viscosity of ILs was predicted using group contribution method with artificial neural network; [15] and the signs of specific optical rotations of chiral ILs were qualitatively predicted to assign absolute configuration, as well as their values were quantitatively predicted by several of the authors. [16] The quantitative structural-activity relationship studies (QSAR) have been used to predict the toxicity of ILs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Chen

et al. 2020

Molecular Informatics

Self Cite

View full text Add to dashboard Cite

Ionic liquids as green solvents have been paid extensive attention in recent years. However, mostly it is cost and time-consuming to measure their properties. Thus, theorical methods, especially ultrafast chemoinformatics methods were introduced into these studies. Instead of abstract and complex models in some QSPR studies, in this study, the 2D structural features related to the toxicity of ionic liquids were discussed at first, and then the corresponding intuitive and meaningful descriptors were suggested to construct quantitative chemoinformatics models, finally a multiple linear regression (MLR) based on the empirical-like models were applied to the estimation of toxicities of 304 ionic liquids. For the test sets, the relationship coefficients reached up to R = 0.90. An external test set of 11 ionic liquids collected from other literatures was submitted to the achieved MLR equations, and the satisfactory result (R = 0.94) was obtained.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Chen

et al. 2020

Molecular Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our research groups have contributed to the development of conformation-independent and conformation-dependent chirality codes, physicochemical atomic stereo descriptors (PAS), and simplified derivatives. − These have been applied to chiral structure–property relationships concerning the assignment of absolute configurations from NMR data, the prediction of enantiospecific or enantioselective reactions, the virtual screening of chiral catalysts for asymmetric reactions, the prediction of the elution order in chiral separations, and the estimation of specific optical rotation in small data sets. , …”

Section: Introductionmentioning

confidence: 99%

“…Successful ML models can be applied to curate the databases of published experimental values or to trigger alert for suspicious experimental values before their disclosure. QSPR has been applied to predict optical rotations using small data sets of congeneric series. ,, …”

Section: Introductionmentioning

confidence: 99%

“…10−12 These have been applied to chiral structure−property relationships concerning the assignment of absolute configurations from NMR data, 13 the prediction of enantiospecific or enantioselective reactions, 14 the virtual screening of chiral catalysts for asymmetric reactions, 15 the prediction of the elution order in chiral separations, and the estimation of specific optical rotation in small data sets. 16,17 Chiral molecules rotate the plane of polarization of linearly polarized radiation (optical rotation). Such a property is used to determine the absolute configuration of unknown compounds based on experimental measurements with a polarimeter and their result comparison with theoretical calculations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation

Mamede

de-Almeida

Chen

et al. 2020

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and their corresponding optical rotation were retrieved from Reaxys and processed independently for three solvents: dichloromethane, chloroform, and methanol. The molecular structures were represented by chiral descriptors based on the physicochemical and topological properties of ligands attached to the chiral center. The sign of optical rotation was predicted by random forests (RF) and artificial neural networks for independent test sets with an accuracy of up to 75% for dichloromethane, 82% for chloroform, and 82% for methanol. RF probabilities and the availability of structures in the training set with the same spheres of atom types around the chiral center defined applicability domains in which the accuracy is higher.

show abstract

“…In particular, many authors have worked on constructing graph-theoretic polynomials based on which some of these topological indices can be found [14][15][16][17][18][19][20][21]. For various applications of topological indices, we cite [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2020

Lett Appl NanoBioSci

View full text Add to dashboard Cite

A molecular graph or a chemical graph is a graph related to the structure of a chemical compound. The topological indices play a vital role in understanding the physical, chemical, and topological properties of the respective compound. ln this article, we discuss the computation of the degree-based topological indices, namely -the fifth −Zagreb indices and their polynomials, fifth hyper −Zagreb indices and their polynomials, general fifth −Zagreb indices and their polynomials, third Zagreb index and it is polynomial for the 4 8 ( )[ , ] lattice, its subdivision, and para-line graphs.

show abstract

Machine learning to predict the specific optical rotations of chiral fluorinated molecules

Cited by 6 publications

References 27 publications

Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation

Untitled

Contact Info

Product

Resources

About