Artificial intelligence chemistry最新文献

{"title":"Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI","authors":"Eslam G. Al-Sakkari ,&nbsp;Ahmed Ragab ,&nbsp;Marzouk Benali ,&nbsp;Olumoye Ajao ,&nbsp;Daria C. Boffito ,&nbsp;Hanane Dagdougui","doi":"10.1016/j.aichem.2025.100106","DOIUrl":"10.1016/j.aichem.2025.100106","url":null,"abstract":"<div><div>Carbon capture, utilization and storage (CCUS), along with lignocellulosic biomass valorization (e.g., lignin, cellulose), are promising decarbonization strategies for hard-to-abate industries. Green solvents, such as deep eutectic solvents and ionic liquids, enable efficient CO₂ capture and selective lignin extraction, enhancing lignin depolymerization into high-value products. However, current molecular design tools are slow and computationally expensive, limiting green material innovation. This study introduces a novel data-driven framework for green material discovery using generative AI, including transformers, generative adversarial networks, and variational autoencoders. The generation process was guided by rule-based and physics- and chemistry-informed models for automatic labeling, with feedback loops to reduce invalid SMILES strings. The approach achieved 70 % molecular validity and 94 % novelty in generating new solvents for CO₂ capture and lignin applications. Model training averaged under one hour, and molecule generation took only seconds, significantly faster than traditional methods. Ensemble machine learning models assessed the environmental sustainability of candidates, and retrosynthesis analysis identified feasible, green synthesis pathways. This flexible, scalable methodology extends beyond solvent discovery to broader applications in process design and optimization, enabling the rapid generation of novel and cost-effective process configurations.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100106"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925647","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":"Deep attention for interpretable detection of organic pollutants in water using colloidal SERS","authors":"Anvar Kunanbayev ,&nbsp;Hirotsugu Hiramatsu ,&nbsp;Wei-Liang Chen ,&nbsp;Yu-Chun Huang ,&nbsp;Yu-Ming Chang ,&nbsp;Stefano Rini","doi":"10.1016/j.aichem.2025.100102","DOIUrl":"10.1016/j.aichem.2025.100102","url":null,"abstract":"<div><div>This paper presents an innovative approach to the interpretable detection of organic pollutants dissolved in water – carbendazim, thiacloprid, and acetamiprid – by leveraging self-attention mechanisms to within the deep neural network (DNN) used for detection. The core contribution of our work is demonstrating how attention mechanisms can significantly enhance the interpretability and performance of pollutant detection in water using colloidal Surface-Enhanced Raman Spectroscopy (SERS) measurements. The cornerstone of our methodology is the optimization of the measurement process, aimed not merely at acquiring high-quality signals but at securing a high volume of data that embodies the full spectrum of measurement variability.</div><div>This optimization includes the development of a measurement protocol that involves (i) the fabrication of colloidal silver nanoparticles utilizing the method proposed by Leopold and Lendl, (ii) the aging of the colloidal mixture with the analytes for a predetermined period, and (iii) the SERS measurement settings. Each step is carefully calibrated to maximize the SERS response sensitivity and reproducibility for the detection of the targeted analytes. Building upon this optimized measurement framework, the paper introduces a deep learning algorithm with an embedded attention mechanism designed to focus on the most relevant spectral features for pollutant detection. Unlike traditional machine learning methods, which often lack interpretability, the proposed attention model provides clear insights into which features are deemed most important for the detection task, thereby offering a direct interpretation of the decision-making process of the neural network.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798021","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":"Transformative role of deep learning in Raman spectroscopy-based detection of microplastics and nanoplastics","authors":"Derrick Boateng","doi":"10.1016/j.aichem.2026.100109","DOIUrl":"10.1016/j.aichem.2026.100109","url":null,"abstract":"<div><div>The pervasive presence of microplastics and nanoplastics (MNPs) in aquatic, terrestrial, and atmospheric systems has emerged as an important environmental challenge. Accurate detection and classification of these particles are essential for understanding their sources, fate, and potential risks to ecosystems and human health. Raman spectroscopy has become a leading analytical tool for MNPs characterization owing to its molecular specificity, non-destructive nature, and high spatial resolution. However, conventional Raman analysis often faces difficulties such as spectral noise, fluorescence interference, and the time-consuming nature of manual spectral interpretation. Recent advances in deep learning (DL) have introduced powerful means of enhancing Raman-based analysis by enabling automated, high-throughput, and data-driven spectral analysis. Nevertheless, many DL-based approaches have been validated primarily using controlled laboratory datasets, and their robustness in complex environmental matrices remains limited. This review provides a comprehensive overview of the integration of DL with Raman spectroscopy for the detection and quantification of MNPs. It summarizes current methodological developments, as well as perspectives on hybrid approaches that combine Raman data with complementary analytical techniques. Key achievements are discussed alongside persisting limitations, including domain shifts between reference and environmental spectra, uncertainty in ground-truth labeling, instrumental and inter-laboratory variability, and challenges related to data standardization and model interpretability. Rather than presenting DL as a universal solution, this review adopts a balanced and application-aware perspective, highlighting emerging directions such as transfer learning, self-supervised and federated approaches, and portable artificial intelligence-enhanced Raman platforms for real-time environmental monitoring, while emphasizing the current constraints of real-world deployment.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188316","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":"A scientist's guide to AI-driven molecular discovery","authors":"Jakes Udabe","doi":"10.1016/j.aichem.2026.100107","DOIUrl":"10.1016/j.aichem.2026.100107","url":null,"abstract":"<div><div>Artificial intelligence (AI) is increasingly steering the discovery of functional molecules and materials, but its progress with generative modeling is held back by the messy, mixed-up nature of the experimental data and a scarcity of high-quality ground truth. This review synthesizes recent advances in data curation, representation, and generative modeling for molecular and materials discovery, and proposes a practical four-stage workflow that integrates structured data capture, intelligent featurization, generative design, and closed-loop experimental validation. Core algorithmic families (supervised, semi-supervised, unsupervised, reinforcement learning) and specialized generative architectures (VAEs, GANs, diffusion models, graph-based models) are surveyed, and discuss how each maps to real-world discovery tasks. The enabling infrastructure (e.g.as electronic lab notebooks (ELNs), knowledge graphs, autonomous laboratories) is likewise analyzed and highlight best practices for reproducibility, uncertainty quantification, and ethical safeguards. Finally, a prioritized checklist was provided for researchers and laboratories to adopt AI-compatible infrastructure and describe open challenges (data standards, causal inference, accessibility) to guide future work.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077851","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":"Integrating machine learning with electrochemical sensors for intelligent food safety monitoring","authors":"Aaryashree ,&nbsp;Arti Devi","doi":"10.1016/j.aichem.2025.100105","DOIUrl":"10.1016/j.aichem.2025.100105","url":null,"abstract":"<div><div>The integration of machine learning (ML) with electrochemical sensors is transforming food safety and quality assessment by enabling quick, affordable, and highly sensitive detection of contaminants, adulterants, and spoilage indicators. Traditional electrochemical analysis faces challenges such as overlapping signals, nonlinear sensor responses, and matrix effects, which diminish accuracy and scalability. ML algorithms offer advanced data processing, feature extraction, and predictive modeling, significantly enhancing detection sensitivity, classification accuracy, and supporting real-time decision-making. This review explores the combined use of ML and electrochemical sensing in food analysis, focusing on key areas like pesticide and heavy metal detection, food authentication, shelf-life prediction, and microbial safety monitoring. It provides a comprehensive range of ML techniques, from basic algorithms like Support Vector Machines and Random Forests to advanced deep learning architectures, including Convolutional Neural Networks, Transformers, and Graph Neural Networks. Additionally, it highlights innovative applications and addresses critical challenges in real-world deployment, such as data scarcity, model generalizability, and the “black box” problem of interpretability. Strategies such as data augmentation, transfer learning, and explainable AI (XAI) are emerging as crucial solutions to enhance data availability and model transparency. The field is also advancing toward adaptive learning frameworks and integration with the Internet of Things (IoT), enabling continuous, networked monitoring throughout the food supply chain. By emphasizing both technical innovations and practical challenges, this review offers a solid foundation for researchers and professionals working at the intersection of electrochemical sensing, machine learning, and food safety analytics.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884196","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":"Quantum-chemistry informed Bayesian optimization for the accelerated discovery of novel pyrazole-based energetic materials","authors":"Muktar Musa Ibrahim,&nbsp;Adamu Uzairu,&nbsp;Gideon Adamu Shallangwa,&nbsp;Muhammad Tukur Ibrahim","doi":"10.1016/j.aichem.2026.100110","DOIUrl":"10.1016/j.aichem.2026.100110","url":null,"abstract":"<div><div>Developing high-performance Energetic Materials (EMs) with small environmental footprint remains a critical challenge for both civilian and military applications. The conventional EMs such as Research Department Explosive (RDX) and trinitrotoluene (TNT) exhibit strong performance, but they tend to release toxic byproducts, posing significant environmental risk. In this work, we introduce a quantum-chemistry informed Bayesian Optimization (BO) for accelerated discovery of novel pyrazole-based EMs. The employed BO and Quantum chemistry calculations to identify pyrazole derivatives with high potential for EMs applications. Using a library of 350 pyrazole-based EMs generated through systematic enumeration of pyrazole scaffold with various explosophoric groups, and their computed density values. BO was employed to rapidly identify pyrazole derivatives with optimal density values in the chemical space within only few iterations. The energetic potential of the BO-selected pyrazole derivatives was further ascertained using density functional theory (DFT) calculations at the B3LYP/6-311G (d, p) level of theory. The generalizability of the BO framework to rapidly identify pyrazole derivatives with high energetic potential was further validated using an expanded library of 1500 pyrazole derivatives. The top five candidates identified by the BO algorithm demonstrates impressive energetic potential with DFT computed detonation parameters comparable or exceeding those of benchmarked explosives. The DFT computed detonation parameters of the BO-selected pyrazole derivatives including crystalline density (1.93–2.25 g cm⁻³), heats of formation (712.4–876.2 kJ mol⁻¹), detonation velocities (8.78–9.22 km s⁻¹), and detonation pressure (18.60–25.11 GPa) were found to be favorable for EMs applications. This study establishes a data-driven workflow for rapid EMs discovery and highlights pyrazole scaffolds as promising platforms for safer, greener EMs, with direct relevance for civilian and military applications.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188317","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":"From potential to practice: The prospective and pitfalls of generative AI and deep learning in molecular simulations","authors":"Rahul D. Jawarkar ,&nbsp;Prashant K. Deshmukh ,&nbsp;Bhavesh Mandwale ,&nbsp;Long Chaiou Ming","doi":"10.1016/j.aichem.2026.100108","DOIUrl":"10.1016/j.aichem.2026.100108","url":null,"abstract":"<div><div>Generative AI and deep learning improve molecular simulations and drug development. Traditional computational methods like MD, MC, and QM/MM have been crucial in investigating biomolecular interactions and thermodynamics. However, processing power and speed restrict their scalability. This article provides a comprehensive review and comparative analysis of how advanced neural network architectures and generative AI models address these computational limitations. This review analyses how advanced neural network architectures and generative AI models satisfy these restrictions. Neural network potentials trained on high-quality quantum datasets achieve ab initio precision at low processing cost. We tested convolutional (CNNs), recurrent (RNNs), graph neural networks (GNNs), and transformers to evaluate how well they could describe molecular changes over time and predict structural changes. Researchers have investigated generative frameworks including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models to develop medications with superior binding affinity and pharmacokinetic characteristics. The findings reveal that AI-driven modelling and physics-based simulations create a closed-loop system where MD or QM/MM simulations enhance AI-generated molecules repeatedly. This feedback loop speeds up hit-to-lead optimisation, increases ADMET prediction, and enhances protein folding and shape information. This paradigm shift from descriptive to predictive and generative frameworks using AI and molecular modelling improves computational drug discovery's scalability, interpretability, and creativity. AI is used as a computational tool and a collaborator to speed up molecular discovery. Overall, this manuscript serves as a critical review summarizing state-of-the-art progress, challenges, and future prospects at the interface of AI and molecular simulation research.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077763","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":"A computational pipeline combining machine learning and molecular simulations identifies repurposed PI3Kα inhibitors from FDA-approved drugs","authors":"Prince Danan Biniyam ,&nbsp;Naomi Kayeri ,&nbsp;Seth Junior Ayihi ,&nbsp;Syed Arslan Haider ,&nbsp;Hope Dzitorwoko ,&nbsp;Kwabena Adu-Adjei ,&nbsp;Michael Osei ,&nbsp;Victoria Ohene-Adu ,&nbsp;Portia Frimpong ,&nbsp;Clifford Tignangnen Penajah ,&nbsp;Silas Boakye Amissah ,&nbsp;Nimra Mazhar ,&nbsp;Cedric Dzidzor Kodjo Amengor","doi":"10.1016/j.aichem.2026.100111","DOIUrl":"10.1016/j.aichem.2026.100111","url":null,"abstract":"<div><div>The computational repurposing of existing drugs has proven to be a fast-track strategy in the development of new cancer therapy. Nonetheless, integrated mechanistically grounded approaches are required for a more reliable in silico identification of new drug-target pairs. In this study, we introduce an integrated workflow combining machine learning with molecular docking, kinase selectivity profiling, molecular dynamics simulations, and ADMET assessment to systematically repurpose FDA-approved drugs as inhibitors of the oncogenic kinase PI3Kα. A robust quantitative structure-activity relationship (QSAR) model (test set R² = 0.825) prioritized candidates from a library of 2458 approved drugs. Through this pipeline, three promising candidates (Vemurafenib, Fedratinib, and Zafirlukast) were identified, each exhibiting stable interactions with PI3Kα, favorable binding free energies, and targeted polypharmacology rather than promiscuous inhibition. Molecular dynamics simulations confirmed that ligand binding reduces protein flexibility and confines the conformational landscape. These results provide a multi-layered computational rationale for repurposing these FDA-approved drugs as PI3Kα inhibitors. Overall, this hybrid approach illustrates how combining data-driven and physics-based methods can enhance the precision of computational drug repurposing, effectively transforming the large library of approved drugs into a tractable source for novel targeted cancer therapies.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188318","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":"Statistical comparison and uncertainty analysis of graph neural networks and machine learning models for molecular property prediction in drug discovery","authors":"Sarmad Waleed ,&nbsp;Shams ul Islam ,&nbsp;Muhammad Saleem ,&nbsp;Ali Ahmed","doi":"10.1016/j.aichem.2025.100103","DOIUrl":"10.1016/j.aichem.2025.100103","url":null,"abstract":"<div><h3>Background:</h3><div>Accurate prediction of molecular properties is essential for accelerating drug discovery. While graph neural networks (GNNs) have emerged as a powerful tool for this task, they have not been systematically benchmarked against traditional machine learning methods, particularly regarding the crucial aspects of predictive accuracy, interpretability, and uncertainty.</div></div><div><h3>Objective:</h3><div>To systematically evaluate state-of-the-art GNN architectures against classical machine learning methods for predicting key physicochemical properties. This study provides a multi-faceted comparison of model performance, statistical robustness, prediction uncertainty, and chemical interpretability.</div></div><div><h3>Methods:</h3><div>We implemented and compared seven models: Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), Graph Isomorphism Networks (GIN), SimpleGNN, Support Vector Regression (SVR), Random Forest, and ElasticNet. These were evaluated across three MoleculeNet datasets: ESOL (aqueous solubility), FreeSolv (hydration free energy), and Lipophilicity (partition coefficient). The evaluation framework included rigorous statistical testing, bootstrap-based uncertainty quantification, and analysis of GAT attention mechanisms for chemical insight.</div></div><div><h3>Results:</h3><div>GAT consistently achieved superior performance, with test RMSE values of 0.1863 (ESOL), 0.1953 (FreeSolv), and 0.4922 (Lipophilicity), outperforming traditional methods by a significant margin. GNNs demonstrated substantial advantages over classical approaches, which showed considerably higher prediction errors. Furthermore, GAT provided the most reliable predictions with the lowest uncertainty and generated chemically relevant insights through its attention mechanism, successfully identifying key functional groups driving molecular properties.</div></div><div><h3>Conclusions:</h3><div>This systematic evaluation provides compelling evidence for the superiority of GNNs, particularly GAT, over traditional machine learning for molecular property prediction. GAT’s high accuracy, combined with its robust uncertainty quantification and chemical interpretability, establishes it as a preferred computational approach for pharmaceutical research and development.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798022","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":"Computational design and immunoinformatic evaluation of a multi-epitope vaccine candidate against Border Disease Virus","authors":"Salman Khan ,&nbsp;Nisar Ahmad ,&nbsp;Sami Ullah ,&nbsp;Liaqat Ali ,&nbsp;Sajjad Ahmad ,&nbsp;Hina Fazal","doi":"10.1016/j.aichem.2025.100104","DOIUrl":"10.1016/j.aichem.2025.100104","url":null,"abstract":"<div><div>Border Disease Virus (BDV), a Flaviviridae pestivirus, cause major reproductive and financial losses to small ruminants, and no licensed vaccine is currently available. In this study, a multi-epitope vaccine (MEV) against BDV was designed using immunoinformatics approach. The construct exhibited favorable physiochemical properties, including an aliphatic index of 68.02, solubility probability of 0.96, and overall stability. It contained multiple high-scoring linear and conformational B-cell epitopes and showed strong predicted binding to MHC class I/II molecules. Molecular docking with TLR-4 revealed stable interactions (binding score: − 312.73). Immune simulations indicated robust primary IgM and secondary IgG responses with memory B- and T-cell formation. Codon optimization confirmed high expression potential in <em>E. coli</em>, (CAI: 1.0; GC content: 61 %), and in-silico cloning indicate vector compatibility. These results suggest that the proposed MEV has potential to induce both humoral and cellular immunity. Further experimental validation is recommended to confirm safety, immunogenicity, and protective efficacy.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"4 1","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840726","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}