Artificial intelligence chemistry最新文献

{"title":"Spatial-temporal self-attention network based on bayesian optimization for light olefins yields prediction in methanol-to-olefins process","authors":"Jibin Zhou ,&nbsp;Duiping Liu ,&nbsp;Mao Ye ,&nbsp;Zhongmin Liu","doi":"10.1016/j.aichem.2024.100067","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100067","url":null,"abstract":"<div><p>Methanol-to-olefins (MTO), as an alternative pathway for the synthesis of light olefins (ethylene and propylene), has gained extensive attention. Accurate prediction of light olefins yields can effectively facilitate process monitoring and optimization, as they are significant economic indexes and stable operation indicators of the industrial MTO process. However, the nonlinearity and dynamic interactions among process variables pose challenges for the prediction using traditional statistical methods. Additionally, physical-based methods relying on first-principle theory are always limited by an insufficient understanding of reaction mechanisms. In contrast, data-driven methods offer a viable solution for the prediction based solely on process data without requiring extensive process knowledge. Therefore, in this work, a data-driven approach that integrates spatial and temporal self-attention modules is proposed to capture complex interactions. Furthermore, Bayesian optimization is employed to determine the optimum hyperparameters and enhance the accuracy of the model. Studies on an actual MTO process demonstrate the superior prediction performance of the proposed model compared to baseline models. Specifically, 24 process variables are selected as the high-dimensional inputs, and yields of ethylene and propylene, as the low-dimensional outputs, are successfully predicted at various prediction horizons ranging from 2 to 8 h.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100067"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000253/pdfft?md5=23aede3f145af7617d071f10a10c1e3f&pid=1-s2.0-S2949747724000253-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemical space navigation by machine learning models for discovering selective MAO-B enzyme inhibitors for Parkinson’s disease","authors":"P. Catherene Tomy,&nbsp;C. Gopi Mohan","doi":"10.1016/j.aichem.2023.100012","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100012","url":null,"abstract":"<div><p>Monoamine Oxidase-B (MAO-B) is a key neuroprotective target that breaks neurotransmitters such as dopamine and releases highly reactive free radicals as the by-product. Its over-expression in the brain observed due to ageing and neurodegenerative diseases contributes to worsening neuronal degeneration. Being the primary enzyme for dopamine metabolism in <em>the substantia nigra</em> of the brain and due to the lack of efficient drug candidates, MAO-B selective, reversible inhibition is hot topic of research in Parkinson’s disease (PD). This study developed machine learning (ML) models that predict the activity of experimentally tested indole and indazole derivatives against MAO-B using linear genetic function approximation (GFA) and two non-linear support vector machine (SVM) and artificial neural network (ANN) techniques. ANN model with an R² of 0.9704 for the training dataset, <span><math><mrow><msup><mrow><mi>q</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace></mrow></math></span>of 0.9436 for cross-validation and <span><math><mrow><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace></mrow></math></span>of 0.9025 for the test dataset were identified as the best-performing ML model with the seven significant molecular descriptors CATS2D_04_DA, CATS2D_05_DA, CATS3D_06_LL, Mor04u, Mor25m, P_VSA_v_2 and nO. The robust ML model was then employed to design novel MAO-B inhibitors with similar core scaffolds and their biological activity prediction. ANN model was further employed in the virtual screening of 4356 molecules from the ChEMBL database. Applicability domain analysis and pharmacokinetic and toxicity profiles predicted three newly designed molecules (22 N, 23 N and 24 N) and two virtually screened best ChEMBL molecules as potential drug candidates using the ANN ML model. Molecular docking studies of the best-identified compounds were performed to understand the molecular mechanism of interactions having high binding energy and selectivity with the MAO-B enzyme. The current study shortlisted 5 potential lead compounds as potent and selective MAO-B inhibitors, which could further be carried forward for in vitro and in vivo studies to discover small molecules against neurodegenerative disease.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100012"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of potential antiviral lead inhibitors against SARS-CoV-2 main protease: Structure-guided virtual screening, docking, ADME, and MD Simulation based approach","authors":"Goverdhan Lanka ,&nbsp;Revanth Bathula ,&nbsp;Balaram Ghosh ,&nbsp;Sarita Rajender Potlapally","doi":"10.1016/j.aichem.2023.100015","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100015","url":null,"abstract":"<div><p>The novel coronavirus disease (COVID-19) was caused by a new strain of the virus SARS-CoV-2 in December 2019 emerged as deadly pandemic that affected millions of people worldwide. Factors such as lack of effective drugs, vaccine resistance, gene mutations, and cost of repurposed drugs demand new potential inhibitors. The main protease (Mpro) of SARS-CoV-2 has a key role in viral replication and transcription and is considered as drug target for new lead identification. In this present work, structure-based virtual screening, docking, MM/GBSA, AutoDock, ADME, and MD simulations-based optimization was proposed for the identification of new potential inhibitors against Mpro of SARS-CoV-2. The ligand molecules M1, M3, and M6 were identified as potential leads from lead optimization. Induced fit docking was performed for the identification of the best poses of lead molecules. The best docked poses of potential leads M1 and M3 were subject to 100 ns MD simulations for the evaluation of stability and interaction analysis into Mpro active site. The structures of the top two leads M1 and M3 were optimized based on MD simulation conformational changes and isoster scanning, designed as new leads M7 and M8. The MD simulation trajectories RMSD, RMSF, protein-ligand, ligand-protein interaction plots, and ligand torsion profiles were analyzed for stability interpretation. The docked complexes of M7 and M8 of Mpro exhibited equilibrated and converged plots in 100 ns simulation. The lead molecules M1, M3, M7, and M8 were identified as potential SARS-CoV-2 inhibitors for COVID-19 disease. A comparative docking study was carried out using FDA-approved drugs to support the potential binding affinities of newly identified lead inhibitors.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100015"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting anti-SARS-CoV-2 activities of chemical compounds using machine learning models","authors":"Beihong Ji,&nbsp;Yuhui Wu,&nbsp;Elena N. Thomas,&nbsp;Jocelyn N. Edwards,&nbsp;Xibing He,&nbsp;Junmei Wang","doi":"10.1016/j.aichem.2023.100029","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100029","url":null,"abstract":"<div><p>To accelerate the discovery of novel drug candidates for Coronavirus Disease 2019 (COVID-19) therapeutics, we reported a series of machine learning (ML)-based models to accurately predict the anti-SARS-CoV-2 activities of screening compounds. We explored 6 popular ML algorithms in combination with 15 molecular descriptors for molecular structures from 9 screening assays in the COVID-19 OpenData Portal hosted by NCATS. As a result, the models constructed by k-nearest neighbors (KNN) using the molecular descriptor GAFF+RDKit achieved the best overall performance with the highest average accuracy of 0.68 and relatively high average area under the receiver operating characteristic curve of 0.74, better than other ML algorithms. Meanwhile, The KNN model for all assays using GAFF+RDKit descriptor outperformed using other descriptors. The overall performance of our developed models was better than REDIAL-2020 (<strong>R</strong>). A web server (<span>https://clickff.org/amberweb/covid-19-cp</span><svg><path></path></svg>) was developed to enable users to predict anti-SARS-CoV-2 activities of arbitrary compounds using the COVID-19-CP (<strong>P</strong>) models. Besides the descriptor-based machine learning models, we also developed graph-based Attentive FP (<strong>A</strong>) models for the 9 assays. We found that the Attentive FP models achieved a comparable performance to that of COVID-19-CP and outperformed the REDIAL-2020 models. The consensus prediction utilizing both COVID-19-CP and Attentive FP can significantly boost the prediction accuracy as assessed by comparing its performance with other three individual models (<strong>R</strong>, <strong>P</strong>, <strong>A</strong>) utilizing the Wilcoxon signed-rank test, thus can ultimately improve the success rate of COVID-19 drug discovery.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000295/pdfft?md5=6026439e3da02cfb256ffaa4b8f13538&pid=1-s2.0-S2949747723000295-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138436572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning small molecule properties in drug discovery","authors":"Nikolai Schapin ,&nbsp;Maciej Majewski ,&nbsp;Alejandro Varela-Rial ,&nbsp;Carlos Arroniz ,&nbsp;Gianni De Fabritiis","doi":"10.1016/j.aichem.2023.100020","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100020","url":null,"abstract":"<div><p>Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced for this purpose in recent years. We review a wide range of properties, including binding affinities, solubility, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss existing popular datasets and molecular descriptors and embeddings, such as chemical fingerprints and graph-based neural networks. We highlight also challenges of predicting and optimizing multiple properties during hit-to-lead and lead optimization stages of drug discovery and explore briefly possible multi-objective optimization techniques that can be used to balance diverse properties while optimizing lead candidates. Finally, techniques to provide an understanding of model predictions, especially for critical decision-making in drug discovery are assessed. Overall, this review provides insights into the landscape of ML models for small molecule property predictions in drug discovery. So far, there are multiple diverse approaches, but their performances are often comparable. Neural networks, while more flexible, do not always outperform simpler models. This shows that the availability of high-quality training data remains crucial for training accurate models and there is a need for standardized benchmarks, additional performance metrics, and best practices to enable richer comparisons between the different techniques and models that can shed a better light on the differences between the many techniques.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100020"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000209/pdfft?md5=3bda0f36e8c7232bba9ee7512ab052fa&pid=1-s2.0-S2949747723000209-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing Wigner sampling and geometry interpolation for deep neural networks learning photochemical reactions","authors":"Li Wang,&nbsp;Zhendong Li,&nbsp;Jingbai Li","doi":"10.1016/j.aichem.2023.100018","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100018","url":null,"abstract":"<div><p>Machine learning photodynamics simulations are revolutionary tools to resolve elusive photochemical reaction mechanisms with time-dependent high-fidelity structure information. Besides the recent advances in neural networks (NNs) potentials, it still lacks a general rule for designing training data for learning photochemical reaction mechanisms with Wigner sampling and geometry interpolation. We present an in-depth investigation of the relationship between the accuracy of the multiple layer NNs and the combinations of training data based on the Wigner sampling and geometry interpolation using model photochemical reactions of the [3]-ladderdiene systems. The NNs trained with Wigner sampling data show underfitting, where the NN errors increase with the structural complexity and diversity. The NNs trained with composite Wigner sampling and geometry interpolation data show one magnitude reduced errors, suggesting an essential role of geometry interpolation in facilitating NNs learning the potential energy surfaces. However, increasing the interpolation steps results in overfitting if the Wigner sampled configuration space is narrowed. Correlating the mean absolute errors (MAE) of the NN predicted energies for the sampled and out-of-sample structures shows an optimal combination ratio of 100:10 between the Wigner sampling structures and geometry interpolation steps for 1000 training data, where the MAE of the sampled structures achieve chemical accuracy while the MAE of the out-of-sample structures is minimized. The NNs trained with the optimally combined data can detect the out-of-sample structures in adaptive sampling with a positive correlation between the maximum standard deviation and MAE of the predicted energies. Collectively, our findings suggest a general rule for designing the training data for ML photodynamics.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100018"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000180/pdfft?md5=2cdb8ecc2616508d396111c8c149852d&pid=1-s2.0-S2949747723000180-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92047094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable graph representation learning model for accurate predictions of drugs aqueous solubility","authors":"Qiufen Chen ,&nbsp;Yuewei Zhang ,&nbsp;Peng Gao ,&nbsp;Jun Zhang","doi":"10.1016/j.aichem.2023.100010","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100010","url":null,"abstract":"<div><p>As increasingly more data science-driven approaches have been applied for compound properties predictions in the domain of drug discovery, such kinds of methods have displayed considerable accuracy compared to conventional ones. In this work, we proposed an interpretable graph learning representation model, SolubNet, for drug aqueous solubility prediction. The comprehensive evaluation demonstrated that SolubNet can successfully capture the quantitative structure-property relationship and can be interpreted with layer-wise relevance propagation (LRP) algorithm regarding how prediction values are generated from original input structures. The key advantage of SolubNet lies in the fact that it includes 3 layers of Topology Adaptive Graph Convolutional Networks which can efficiently perceive chemical local environments. SolubNet showed high performance in several tasks for drugs’ aqueous solubility prediction. LRP revealed that SolubNet can identify high and low polar regions of a given molecule, assigning them reasonable weights to predict the final solubility, in a way highly compatible with chemists’ intuition. We are confident that such a flexible yet interpretable and accurate tool will largely enhance the efficiency of drug discovery, and will even contribute to the methodology development of computational pharmaceutics.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100010"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49764057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning software to learn negligible elements of the Hamiltonian matrix","authors":"Chen Qu ,&nbsp;Paul L. Houston ,&nbsp;Qi Yu ,&nbsp;Priyanka Pandey ,&nbsp;Riccardo Conte ,&nbsp;Apurba Nandi ,&nbsp;Joel M. Bowman","doi":"10.1016/j.aichem.2023.100025","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100025","url":null,"abstract":"<div><p>As a follow-up to our recent Communication in the Journal of Chemical Physics [J. Chem. Phys. 159 071101 (2023)], we report and make available the Jupyter Notebook software here. This software performs binary machine learning classification (MLC) with the goal of learning negligible Hamiltonian matrix elements for vibrational dynamics. We illustrate its usefulness for a Hamiltonian matrix for H₂O by using three MLC algorithms: Random Forest, Support Vector Machine, and Multi-layer Perceptron.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100025"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000258/pdfft?md5=aae23141726aebcb5969aecabfb1ff8f&pid=1-s2.0-S2949747723000258-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138430215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning classification model for cholesterol-lowering peptides","authors":"Jose Isagani B. Janairo","doi":"10.1016/j.aichem.2023.100026","DOIUrl":"10.1016/j.aichem.2023.100026","url":null,"abstract":"<div><p>Cholesterol-lowering peptides (CLPs) are bioactive biomolecules often derived from food proteins. These short peptides bind with bile acids leading to decreased intestinal absorption of cholesterol. CLPs are promising bioceuticals that can possibly be used to support interventions for the management of high cholesterol. Integrating machine learning (ML) in the screening and discovery workflow for CLP can reduce trial-and-error thereby accelerating and increase the efficiency of the overall process. In this study, a support vector machine model that can distinguish CLPs from non-CLPs is presented. The model was built on a diverse dataset of 1840 peptides, with sequence length that ranges from 4 to 7. The ML model only needs 8 features (VHSE scores), and the most important features were found to be related to peptide polarity and hydrophobicity based on feature importance analysis utilizing Shapley and permutation-based method. The formulated ML classifier is reliable, as demonstrated by AUC &gt;0.7 for a diverse test dataset and AUC &gt;0.9 for a conservative validation dataset composed mainly of the top and bottom CLPs. Overall, the presented ML model presents incremental yet meaningful advances to the application of ML for understanding the nature of CLPs, and their discovery and development.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100026"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294974772300026X/pdfft?md5=0835f2ca55b7c8185903061e3f9f59c0&pid=1-s2.0-S294974772300026X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An accurate full-dimensional interaction potential energy surface of CO2+N2 incorporating ∆-machine learning approach via permutation invariant polynomial-neural network","authors":"Jia Li,&nbsp;Jun Li","doi":"10.1016/j.aichem.2023.100019","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100019","url":null,"abstract":"<div><p>The interaction between CO₂ and N₂, both as essential components of the Earth’s atmosphere, plays a crucial role in investigating the greenhouse effect. In this work, we sampled 40,930 data points within the full-dimensional configuration space of CO₂ and N₂ and performed calculations at the level of explicitly correlated coupled cluster single, double, and perturbative triple level with the augmented correlation corrected valence triple-ζ basis set (CCSD(T)-F12a/AVTZ). To ensure computational accuracy while reducing computational costs, we employed the recently proposed Δ-machine learning (Δ-ML) method based on Permutation Invariant Polynomial-Neural Network (PIP-NN) for basis set superposition error (BSSE) correction. By leveraging the limited extrapolation capability of NN, efficient sampling was performed within the existing dataset, enabling the construction of the potential energy surface (PES) incorporating BSSE correction with only a small number of data points for BSSE calculations. A total of approximately 1100 data points were selected from the initial dataset to construct a BSSE correction PES. Utilizing this correction PES, BSSE predictions were carried out for all remaining data points, resulting in the successful development of a high-precision full-dimensional PES with BSSE correction for the CO₂ + N₂ system. The PIP-NN based Δ-ML method significantly reduced the required BSSE calculations by approximately 97.2%, resulting in a final PES with a fitting error of merely 0.026 kcal/mol.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"1 2","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000192/pdfft?md5=4f0503b66010517c20f46da9e39da648&pid=1-s2.0-S2949747723000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92061993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}