Prediction of the Aqueous Solubility of Compounds Based on Light Gradient Boosting Machines with Molecular Fingerprints and the Cuckoo Search Algorithm

Li, Mengshan; Chen, Huijie; Zhang, Hang; Zeng, Ming; Chen, Bingsheng; Guan, Lixin

doi:10.1021/acsomega.2c03885

Cited by 10 publications

(9 citation statements)

References 38 publications

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“…The schematic representation of the GBM model is shown in Figure . Due to the desirable properties of GBM and its excellent prediction capabilities, GBM is being increasingly used in different fields of science and engineering. − Recently Li et al employed different versions of GBM for prediction of aqueous solubility of compounds.…”

Section: Brief Descriptions Of Gradient Boosting Machines (Gbm) Suppo...mentioning

confidence: 99%

Development of Gradient Boosting Machines for Estimation of Total and Dynamic Liquid Holdup in Trickle Bed Reactor

Bhosale

Pandya

Chatterjee

et al. 2023

Ind. Eng. Chem. Res.

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Prediction of liquid holdup is of significance in designing and in evaluating the performance of trickle bed contactors. The present work focuses on the development of Gradient Boosting Machines (GBM) for the prediction of total and dynamic liquid holdup in trickle bed reactors. A comprehensive data set of 394 data points of total liquid holdup and 416 data points of dynamic liquid holdup curated from open literature is used in this study. We built GBM models with the input data sets containing 11 governing variables. GBM provided excellent predictions for both data sets. We have also compared the GBM predictions with that of the Random Forest (RF) and Artificial Neural Networks (ANN) predictions. As GBM provided the best performance, we further employed SHAP (SHapley Additive exPlanations) with GBM black box models to get local and global interpretability. Also, we have used SHAP to identify informative subsets of governing variables. The work shall pave the way for use of GBM in prediction of hydrodynamic parameters in multiphase systems.

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Section: Brief Descriptions Of Gradient Boosting Machines (Gbm) Suppo...mentioning

confidence: 99%

Development of Gradient Boosting Machines for Estimation of Total and Dynamic Liquid Holdup in Trickle Bed Reactor

Bhosale

Pandya

Chatterjee

et al. 2023

Ind. Eng. Chem. Res.

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“…Researchers have used different types of artificial intelligence algorithms to build predictive models of chemicals, including artificial neural network (ANN), light gradient boosting (LightGBM), deep neural networks (DNN), random forest (RF), extra trees (ET), multiple linear regression (MLR), partial least squared (PLS), k-nearest neighbors (k-NN), support vector machine (SVM), and Ridge regression. − For example, Ling et al used the XGBoost algorithm combined with SHapley Additive exPlanation (SHAP) to predict the viscosities of deep eutectic solvents (DESs) at different temperatures . Shen et al used different algorithms to predict properties such as the flash point temperature of substances and obtained accurate results. − However, using chemical simulation software to calculate the viscosities of polymers under different conditions poses some difficulties and challenges.…”

Section: Introductionmentioning

confidence: 99%

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

Wang,

Sadowski,

2024

ACS Sustainable Chem. Eng.

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With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry.

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“…Chinta and Rengaswamy proposed a machine learning-based QSPR-based approach to predict drug solubility in binary solvent systems using structural features, such as molar refractivity, McGowan volume, topological surface area, etc. The CS-LightGBM model was established by Li to predict the aqueous solubility of 2446 organic compounds. This model had a better prediction performance than the other six different models and can be used to solve solubility prediction problems in other fields, such as solvent selection and drug screening.…”

Section: Introductionmentioning

confidence: 99%

Insight into the Relationship of Aqueous Solubility and Single-Crystal Structure of Harmine’s New Organic Salt Forms

Hong,

Wu,

Wang

et al. 2024

Crystal Growth & Design

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Harmine (HAM) is a natural product with neuroprotective and antitumor activity. Its poor aqueous solubility is the major factor limiting its clinical application. In this work, seven new organic acid salts of HAM were prepared and systematically characterized. Their single crystals were obtained, and the structures were identified by a single-crystal X-ray diffractometer. The corresponding solubilities in water were determined in the temperature range of 20−35 °C by the HPLC method. All HAM salts showed significantly improved powder solubility profiles compared with the free base. The relationship between the aqueous solubility and the single-crystal structures of HAM organic salts was explored from several perspectives, including the basic properties of acidic counterions, the intermolecular force in crystals (type of lattice force, cohesive energy density, and lattice energy), the factors affecting the dissolution process (hydration energy and sublimation energy), and the external environmental factors (chemical hardness). The aqueous solubility of different HAM salts was dependent on the type of charge assistant hydrogen bonds; that is, the salts withbonds exhibited better solubility than the other salts with N + −H•••O − hydrogen bonds. In addition, the results revealed that HAM salts with higher acidic counterion intrinsic solubility (S ACID ) showed smaller cohesive energy density of the crystal; the larger the absolute value of hydration energy (ΔG hyd 0 ), the greater the chemical hardness (η) of acidic anions in the crystal and ultimately the better the equilibrium solubility in water. The rank order of the linear fitting accuracy of these four parameters regarding solubility was as follows: Δη > CED > ΔG hyd 0 . The solubility of HAM organic acidic salts is associated with several factors, even though the chemical hardness seems to play a central role.

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Prediction of the Aqueous Solubility of Compounds Based on Light Gradient Boosting Machines with Molecular Fingerprints and the Cuckoo Search Algorithm

Cited by 10 publications

References 38 publications

Development of Gradient Boosting Machines for Estimation of Total and Dynamic Liquid Holdup in Trickle Bed Reactor

Development of Gradient Boosting Machines for Estimation of Total and Dynamic Liquid Holdup in Trickle Bed Reactor

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

Insight into the Relationship of Aqueous Solubility and Single-Crystal Structure of Harmine’s New Organic Salt Forms

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