Journal of Chemical Information and Modeling 最新文献

{"title":"Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.","authors":"Adrian Racki, Kamil Paduszyński","doi":"10.1021/acs.jcim.4c02364","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02364","url":null,"abstract":"<p><p>This paper reviews the recent and most impactful advancements in the application of artificial neural networks in modeling the properties of ionic liquids. As salts that are liquid at temperatures below 100 °C, ionic liquids possess unique properties beneficial for various industrial applications such as carbon capture, catalytic solvents, and lubricant additives. The study emphasizes the challenges in selecting appropriate ILs due to the vast variability in their properties, which depend significantly on their cation and anion structures. The review discusses the advantages of using ANNs, including feed-forward, cascade-forward, convolutional, recurrent, and graph neural networks, over traditional machine learning algorithms for predicting the thermodynamic and physical properties of ILs. The paper also highlights the importance of data preparation, including data collection, feature engineering, and data cleaning, in developing accurate predictive models. Additionally, the review covers the interpretability of these models using techniques such as SHapley Additive exPlanations to understand feature importance. The authors conclude by discussing future opportunities and the potential of combining ANNs with other computational methods to design new ILs with targeted properties.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MultiCTox: Empowering Accurate Cardiotoxicity Prediction through Adaptive Multimodal Learning","authors":"Lin Feng,&nbsp;Xiangzheng Fu,&nbsp;Zhenya Du,&nbsp;Yuting Guo,&nbsp;Linlin Zhuo*,&nbsp;Yan Yang,&nbsp;Dongsheng Cao* and Xiaojun Yao*,&nbsp;","doi":"10.1021/acs.jcim.5c0002210.1021/acs.jcim.5c00022","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00022https://doi.org/10.1021/acs.jcim.5c00022","url":null,"abstract":"<p >Cardiotoxicity refers to the inhibitory effects of drugs on cardiac ion channels. Accurate prediction of cardiotoxicity is crucial yet challenging, as it directly impacts the evaluation of cardiac drug efficacy and safety. Numerous methods have been developed to predict cardiotoxicity, yet their performance remains limited. A key limitation is that these methods often rely solely on single-modal data, making multimodal data integration challenging. As a result, we present a multimodal method integrating molecular SMILES, structure, and fingerprint to enhance cardiotoxicity prediction. First, we designed a fusion layer to unify representations from different modalities. During training, the model maximizes intramodal similarity for the same molecule while minimizing intermolecular similarity, ensuring consistent cross-modal representations. This study evaluates the inhibitory effects of candidate drugs on voltage-gated potassium (hERG), sodium (Nav1.5), and calcium (Cav1.2) channels. Experimental results demonstrate that the proposed model significantly outperforms existing state-of-the-art methods in cardiotoxicity prediction. We anticipate that this model will contribute significantly to the development and safety evaluation of cardiac drugs, reducing cardiotoxicity-related risks.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3517–3528 3517–3528"},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stacking Interactions of Druglike Heterocycles with Nucleobases.","authors":"Audrey V Conner, Lauren M Kim, Patrick A Fagan, Drew P Harding, Steven E Wheeler","doi":"10.1021/acs.jcim.4c02420","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02420","url":null,"abstract":"<p><p>Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks","authors":"Adrian Racki,&nbsp; and ,&nbsp;Kamil Paduszyński*,&nbsp;","doi":"10.1021/acs.jcim.4c0236410.1021/acs.jcim.4c02364","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02364https://doi.org/10.1021/acs.jcim.4c02364","url":null,"abstract":"<p >This paper reviews the recent and most impactful advancements in the application of artificial neural networks in modeling the properties of ionic liquids. As salts that are liquid at temperatures below 100 °C, ionic liquids possess unique properties beneficial for various industrial applications such as carbon capture, catalytic solvents, and lubricant additives. The study emphasizes the challenges in selecting appropriate ILs due to the vast variability in their properties, which depend significantly on their cation and anion structures. The review discusses the advantages of using ANNs, including feed-forward, cascade-forward, convolutional, recurrent, and graph neural networks, over traditional machine learning algorithms for predicting the thermodynamic and physical properties of ILs. The paper also highlights the importance of data preparation, including data collection, feature engineering, and data cleaning, in developing accurate predictive models. Additionally, the review covers the interpretability of these models using techniques such as SHapley Additive exPlanations to understand feature importance. The authors conclude by discussing future opportunities and the potential of combining ANNs with other computational methods to design new ILs with targeted properties.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3161–3175 3161–3175"},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.4c02364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials","authors":"Jinzhe Zeng,&nbsp;Timothy J. Giese,&nbsp;Duo Zhang,&nbsp;Han Wang and Darrin M. York*,&nbsp;","doi":"10.1021/acs.jcim.4c0244110.1021/acs.jcim.4c02441","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02441https://doi.org/10.1021/acs.jcim.4c02441","url":null,"abstract":"<p >Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discovery, enzyme catalysis, and materials design. The current landscape of MLP software presents challenges due to the limited interoperability between packages, which can lead to inconsistent benchmarking practices and necessitates separate interfaces with molecular dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit framework that extends its capabilities to support external graph neural network (GNN) potentials.DeePMD-GNN enables the seamless integration of popular GNN-based models, such as NequIP and MACE, within the DeePMD-kit ecosystem. Furthermore, the new software infrastructure allows GNN models to be used within combined quantum mechanical/molecular mechanical (QM/MM) applications using the range corrected ΔMLP formalism.We demonstrate the application of DeePMD-GNN by performing benchmark calculations of NequIP, MACE, and DPA-2 models developed under consistent training conditions to ensure fair comparison.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3154–3160 3154–3160"},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energetics of Expanded PAM Readability by Engineered Cas9-NG","authors":"Shreya Bhattacharya,&nbsp; and ,&nbsp;Priyadarshi Satpati*,&nbsp;","doi":"10.1021/acs.jcim.5c0001110.1021/acs.jcim.5c00011","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00011https://doi.org/10.1021/acs.jcim.5c00011","url":null,"abstract":"<p >The energetic basis for the enhanced PAM (protospacer adjacent motif) readability in engineered Cas9-NG (a variant of Cas9 from <i>Streptococcus pyogenes</i> (<i>Sp</i>Cas9)) with seven mutations: (R1335V, E1219F, D1135V, L1111R, T1337R, G1218R, and A1322R) remains a fundamental unsolved problem. Utilizing the X-ray structure of the precatalytic complex (<i>Sp</i>Cas9:sgRNA:dsDNA) as a template, we calculated the changes in PAM (TGG, TGA, TGT, or TGC) binding affinity (ΔΔ<i>G</i>) associated with each of the seven mutations in <i>Sp</i>Cas9 through rigorous alchemical simulations (sampling ∼ 53 μs). The underlying thermodynamics (ΔΔ<i>G</i>) accounts for the experimentally observed differences in DNA cleavage activity between <i>Sp</i>Cas9 and Cas9-NG across various DNA substrates. The interaction energies between <i>Sp</i>Cas9 and DNA are significantly influenced by the type and location of the amino acid mutations. Notably, the R1335V mutation disfavors DNA binding by disrupting critical interactions with the PAM. However, the destabilizing effect of the R1335V mutation is mitigated by four advantageous mutations (E1219F, D1135V, L1111R, and T1337R), which primarily introduce nonbase-specific interactions and enhance PAM readability. The hydrophobic substitutions (E1219F and D1135V) are particularly impactful, as they exclude solvent from the PAM binding pocket, strengthening electrostatic interactions in the low dielectric medium and increasing the stability of the noncognate PAM complexes by ∼2–5 kcal/mol. Additionally, L1111R and T1337R facilitate DNA binding by forming direct electrostatic contacts. In contrast, the charge mutations G1218R and A1322R do not effectively promote interactions with the negatively charged DNA, clearly demonstrating that the location of mutations is crucial in shaping these interaction energetics. We demonstrated that stabilization of the Cas9-NG: noncognate PAM complexes enables broader PAM recognition. This is primarily achieved through two mechanisms: (1) the establishment of new nonbase-specific interactions between the protein and nucleotides and (2) the enhancement of electrostatic interactions within a relatively dry and hydrophobic pocket. The findings revealed that mutation-induced desolvation can improve the recognition of noncognate PAMs, paving the way for the rational and innovative design of <i>Sp</i>Cas9 mutants.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3628–3639 3628–3639"},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MultiCTox: Empowering Accurate Cardiotoxicity Prediction through Adaptive Multimodal Learning.","authors":"Lin Feng, Xiangzheng Fu, Zhenya Du, Yuting Guo, Linlin Zhuo, Yan Yang, Dongsheng Cao, Xiaojun Yao","doi":"10.1021/acs.jcim.5c00022","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00022","url":null,"abstract":"<p><p>Cardiotoxicity refers to the inhibitory effects of drugs on cardiac ion channels. Accurate prediction of cardiotoxicity is crucial yet challenging, as it directly impacts the evaluation of cardiac drug efficacy and safety. Numerous methods have been developed to predict cardiotoxicity, yet their performance remains limited. A key limitation is that these methods often rely solely on single-modal data, making multimodal data integration challenging. As a result, we present a multimodal method integrating molecular SMILES, structure, and fingerprint to enhance cardiotoxicity prediction. First, we designed a fusion layer to unify representations from different modalities. During training, the model maximizes intramodal similarity for the same molecule while minimizing intermolecular similarity, ensuring consistent cross-modal representations. This study evaluates the inhibitory effects of candidate drugs on voltage-gated potassium (hERG), sodium (Nav1.5), and calcium (Cav1.2) channels. Experimental results demonstrate that the proposed model significantly outperforms existing state-of-the-art methods in cardiotoxicity prediction. We anticipate that this model will contribute significantly to the development and safety evaluation of cardiac drugs, reducing cardiotoxicity-related risks.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Descriptors for Subunit Interface Regions in Homodimers: Effect of Lipid Membrane and Secondary Structure Type.","authors":"Aslı Yüksek, Batuhan Yıkınç, İrem Nayır, Defne Alnıgeniş, Vahap Gazi Fidan, Tayyip Topuz, Ebru Demet Akten","doi":"10.1021/acs.jcim.4c01233","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01233","url":null,"abstract":"<p><p>A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (C_<i>x</i>), surface planarity, and surface roughness. SASA and C_<i>x</i> distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and C_<i>x</i> values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energetics of Expanded PAM Readability by Engineered Cas9-NG.","authors":"Shreya Bhattacharya, Priyadarshi Satpati","doi":"10.1021/acs.jcim.5c00011","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00011","url":null,"abstract":"<p><p>The energetic basis for the enhanced PAM (protospacer adjacent motif) readability in engineered Cas9-NG (a variant of Cas9 from <i>Streptococcus pyogenes</i> (<i>Sp</i>Cas9)) with seven mutations: (R1335V, E1219F, D1135V, L1111R, T1337R, G1218R, and A1322R) remains a fundamental unsolved problem. Utilizing the X-ray structure of the precatalytic complex (<i>Sp</i>Cas9:sgRNA:dsDNA) as a template, we calculated the changes in PAM (TGG, TGA, TGT, or TGC) binding affinity (ΔΔ<i>G</i>) associated with each of the seven mutations in <i>Sp</i>Cas9 through rigorous alchemical simulations (sampling ∼ 53 μs). The underlying thermodynamics (ΔΔ<i>G</i>) accounts for the experimentally observed differences in DNA cleavage activity between <i>Sp</i>Cas9 and Cas9-NG across various DNA substrates. The interaction energies between <i>Sp</i>Cas9 and DNA are significantly influenced by the type and location of the amino acid mutations. Notably, the R1335V mutation disfavors DNA binding by disrupting critical interactions with the PAM. However, the destabilizing effect of the R1335V mutation is mitigated by four advantageous mutations (E1219F, D1135V, L1111R, and T1337R), which primarily introduce nonbase-specific interactions and enhance PAM readability. The hydrophobic substitutions (E1219F and D1135V) are particularly impactful, as they exclude solvent from the PAM binding pocket, strengthening electrostatic interactions in the low dielectric medium and increasing the stability of the noncognate PAM complexes by ∼2-5 kcal/mol. Additionally, L1111R and T1337R facilitate DNA binding by forming direct electrostatic contacts. In contrast, the charge mutations G1218R and A1322R do not effectively promote interactions with the negatively charged DNA, clearly demonstrating that the location of mutations is crucial in shaping these interaction energetics. We demonstrated that stabilization of the Cas9-NG: noncognate PAM complexes enables broader PAM recognition. This is primarily achieved through two mechanisms: (1) the establishment of new nonbase-specific interactions between the protein and nucleotides and (2) the enhancement of electrostatic interactions within a relatively dry and hydrophobic pocket. The findings revealed that mutation-induced desolvation can improve the recognition of noncognate PAMs, paving the way for the rational and innovative design of <i>Sp</i>Cas9 mutants.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.","authors":"Jinzhe Zeng, Timothy J Giese, Duo Zhang, Han Wang, Darrin M York","doi":"10.1021/acs.jcim.4c02441","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02441","url":null,"abstract":"<p><p>Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discovery, enzyme catalysis, and materials design. The current landscape of MLP software presents challenges due to the limited interoperability between packages, which can lead to inconsistent benchmarking practices and necessitates separate interfaces with molecular dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit framework that extends its capabilities to support external graph neural network (GNN) potentials.DeePMD-GNN enables the seamless integration of popular GNN-based models, such as NequIP and MACE, within the DeePMD-kit ecosystem. Furthermore, the new software infrastructure allows GNN models to be used within combined quantum mechanical/molecular mechanical (QM/MM) applications using the range corrected ΔMLP formalism.We demonstrate the application of DeePMD-GNN by performing benchmark calculations of NequIP, MACE, and DPA-2 models developed under consistent training conditions to ensure fair comparison.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}