Journal of Chemical Information and Modeling 最新文献

{"title":"Structure-Activity Relationship of Ciprofloxacin towards S-Spike Protein of SARS-CoV-2: Synthesis and <i>In-Silico</i> Evaluation.","authors":"Sahil Kumar, Papiya Dey, Arup Kumar Pathak, Amey Wadawale, Dharmendra K Maurya, Kalyani Natu, Kakoli Bose, Dibakar Goswami","doi":"10.1021/acs.jcim.4c00918","DOIUrl":"10.1021/acs.jcim.4c00918","url":null,"abstract":"<p><p>The recent outbreak of the coronavirus (COVID-19) pandemic, caused by the SARS-CoV-2 virus, has posed serious threats to global health systems. Although several directions have been put by the WHO for effective treatment, use of antibiotics, particularly ciprofloxacin, in suspected and acquired Covid-19 patients has raised an even more serious concern of antibiotic resistance. Ciprofloxacin has been reported to inhibit entry of SARS-CoV-2 into the host cells via interacting with the spike (S) protein. However, a proper structure-activity relationship study of ciprofloxacin with the S-protein is lacking, which inhibits researchers from developing a more potent fluoroquinolone analogue, specific for inhibition of SARS-CoV-2 viral entry. Herein, in order to have a structure-activity relationship study, we have accomplished a short and convergent synthesis of different derivatives of ciprofloxacin and a detailed <i>in-silico</i> study using molecular docking to explore the interactions of the derivatives with S-protein. The ADMET studies also indicated the drug likeliness and nontoxicity of the derivatives. Furthermore, the molecular dynamics simulation approach was used to study the dynamical behavior after the best docked derivative binds to the protein, and the MM-PBSA approach was adopted to calculate the binding energies. This has led to a derivative that has higher interactions with the S-protein compared to ciprofloxacin, without hampering the dynamics of the interactions. The strong affinity of compound <b>5</b> with the SARS-CoV-2 spike RBD protein was further evaluated experimentally using biolayer interferometry (BLI). Furthermore, molecular docking and molecular dynamics simulation were extended to evaluate its binding with the mutated variants Delta and Omicron. We anticipate that the current study could lead to an alternative therapeutic viral inhibitor with a better efficacy than ciprofloxacin.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"825-844"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968668","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":"Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.","authors":"David Bushiri Pwesombo, Carsten Beese, Christopher Schmied, Han Sun","doi":"10.1021/acs.jcim.4c00835","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c00835","url":null,"abstract":"<p><p>Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity prediction from Cell Painting image data, we introduce here a semisupervised contrastive (SemiSupCon) learning approach. This approach combines the strengths of using biological annotations in supervised contrastive learning and leveraging large unannotated image data sets with self-supervised contrastive learning. SemiSupCon enhances downstream prediction performance of classifying MeSH pharmacological classifications from PubChem, as well as mode of action and biological target annotations from the Drug Repurposing Hub across two publicly available Cell Painting data sets. Notably, our approach has effectively predicted the biological activities of several unannotated compounds, and these findings were validated through literature searches. This demonstrates that our approach can potentially expedite the exploration of biological activity based on Cell Painting image data with minimal human intervention.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 2","pages":"528-543"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044966","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":"Development of Receptor Desolvation Scoring and Covalent Sampling in DOCK 6: Methods Evaluated on a RAS Test Set.","authors":"Y Stanley Tan, Mayukh Chakrabarti, Reed M Stein, Lauren E Prentis, Robert C Rizzo, Tom Kurtzman, Marcus Fischer, Trent E Balius","doi":"10.1021/acs.jcim.4c01623","DOIUrl":"10.1021/acs.jcim.4c01623","url":null,"abstract":"<p><p>Molecular docking methods are widely used in drug discovery efforts. RAS proteins are important cancer drug targets, and are useful systems for evaluating docking methods, including accounting for solvation effects and covalent small molecule binding. Water often plays a key role in small molecule binding to RAS proteins, and many inhibitors─including FDA-approved drugs─covalently bind to oncogenic RAS proteins. We assembled a RAS test set, consisting of 138 RAS protein structures and 2 structures of KRAS DNA in complex with ligands. In DOCK 6, we have implemented a receptor desolvation scoring function and a covalent docking algorithm. These new features were evaluated using the test set, with pose reproduction, cross-docking, and enrichment calculations. We tested two solvation methods for generating receptor desolvation scoring grids: GIST and 3D-RISM. Using grids from GIST or 3D-RISM, water displacements are precomputed with Gaussian-weighting, and trilinear interpolation is used to speed up this scoring calculation. To test receptor desolvation scoring, we prepared GIST and 3D-RISM grids for all KRAS systems in the test set, and we compare enrichment performance with and without receptor desolvation. Accounting for receptor desolvation using GIST improves enrichment for 51% of systems and worsens enrichment for 35% of systems, while using 3D-RISM improves enrichment for 44% of systems and worsens enrichment for 30% of systems. To more rigorously test accounting for receptor desolvation using 3D-RISM, we compare pose reproduction with and without 3D-RISM receptor desolvation. Pose reproduction docking with 3D-RISM yields a 1.8 ± 2.41% increase in success rate compared to docking without 3D-RISM. Accounting for receptor desolvation provides a small, but significant, improvement in both enrichment and pose reproduction for this set. We tested the covalent attach-and-grow algorithm on 70 KRAS systems containing covalent ligands, obtaining similar pose reproduction success rates between covalent and noncovalent docking. Comparing covalent docking to noncovalent docking, there is a 2.4 ± 3.29% increase and a 1.27 ± 3.33% decline in the success rate when docking with experimental and SMILES-generated ligand conformations, respectively. As a proof-of-concept, we performed covalent virtual screens with and without receptor desolvation scoring, targeting the switch II pocket of KRAS, using 3.4 million make-on-demand acrylamide compounds from the Enamine REAL database. On average, the attach-and-grow algorithm spends approximately 17.61 s per molecule across the screen. The test set is available at https://github.com/tbalius/teb_docking_test_sets.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"722-748"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929896","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":"Activation and Reactivity of the Deubiquitinylase OTU Cezanne-2 from MD Simulations and QM/MM Calculations.","authors":"Metehan Ilter, Andrés M Escorcia, Eric Schulze-Niemand, Michael Naumann, Matthias Stein","doi":"10.1021/acs.jcim.4c01964","DOIUrl":"10.1021/acs.jcim.4c01964","url":null,"abstract":"<p><p>Cezanne-2 (Cez2) is a deubiquitinylating (DUB) enzyme involved in the regulation of ubiquitin-driven cellular signaling and selectively targets Lys11-linked polyubiquitin chains. As a representative member of the ovarian tumor (OTU) subfamily DUBs, it performs cysteine proteolytic isopeptide bond cleavage; however, its exact catalytic mechanism is not yet resolved. In this work, we used different computational approaches to get molecular insights into the Cezanne-2 catalytic mechanism. Extensive molecular dynamics (MD) simulations were performed for 12 μs to model free Cez2 and the diubiquitin (diUb) substrate-bound protein-protein complex in two different charge states of Cez2, each corresponding to a distinct reactive state in its catalytic cycle. The simulations were analyzed in terms of the relevant structural parameters for productive enzymatic catalysis. Reactive diUb-Cez2 complex configurations were identified, which lead to isopeptide bond cleavage and stabilization of the tetrahedral oxyanion intermediate. The reliability of these complexes was further assessed by quantum mechanics/molecular mechanics (QM/MM) optimizations. The results show that Cez2 follows a modified cysteine protease mechanism involving a catalytic Cys210/His367 dyad, with the oxyanion hole to be a part of the \"C-loop,\" and polarization of His367 by the formation of a strictly conserved water bridge with Glu173. The third residue has a dual role in catalysis as it mediates substrate binding and polarization of the catalytic dyad. A similar mechanism was identified for Cezanne-1, the paralogue of Cez2. In general, our simulations provide valuable molecular information that may help in the rational design of selective inhibitors of Cez2 and closely related enzymes.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"921-936"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941454","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":"PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.","authors":"Farzin Sohraby, Jing-Yao Guo, Ariane Nunes-Alves","doi":"10.1021/acs.jcim.4c01656","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01656","url":null,"abstract":"<p><p>Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H₂ from<i>Desulfovibrio fructosovorans</i> [NiFe] hydrogenase as a training set. [NiFe] hydrogenases are receiving increasing attention in biotechnology due to their high efficiency in the generation of H₂, which is considered by many to be the fuel of the future. However, some of these enzymes are sensitive to O₂ and CO. Many efforts have been made to rectify this problem and generate air-stable enzymes by introducing mutations that selectively regulate the access of specific gas molecules to the catalytic site. Herein, we showcase the performance of PathInHydro for the identification of unbinding paths in different test sets, including another gas molecule and a different [NiFe] hydrogenase, which demonstrates its feasibility for the trajectory analysis of a diversity of gas molecules along enzymes with mutations and sequence differences. PathInHydro allows the user to skip time-consuming manual analysis and visual inspection, facilitating data analysis for MD simulations of ligand unbinding from [NiFe] hydrogenases. The codes and data sets are available online: https://github.com/FarzinSohraby/PathInHydro.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 2","pages":"589-602"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044960","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":"Subgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs.","authors":"Ping Xuan, Xiaoying Qi, Sentao Chen, Jing Gu, Xiuju Wang, Hui Cui, Jun Lu, Tiangang Zhang","doi":"10.1021/acs.jcim.4c01757","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01757","url":null,"abstract":"<p><p>As an increasing number of microRNAs (miRNAs) have become biomarkers of various human diseases, prediction of the candidate disease-related miRNAs is helpful for facilitating the early diagnosis of diseases. Most of the recent prediction models concentrated on learning of the features from the heterogeneous graph composed of miRNAs and diseases. However, they failed to fully exploit the subgraph structures consisting of multiple miRNA and disease nodes, and they also did not completely integrate the context relationships among the pairwise features. We proposed a prediction model, SFPred, to integrate and encode the local topologies from neighborhood subgraphs, the dynamically evolved heterogeneous graph topology, and the context among pairwise features. First, the importance of an miRNA (disease) node to another node is formulated according to the subgraphs composed of their neighbors. Second, the features of each miRNA (disease) node continuously change when the graph encoding gradually deepens for the miRNA-disease heterogeneous network. A strategy based on multi-layer perceptron (MLP) is designed to estimate the edge weights according to the changed node features and form the dynamic graph topology. Third, considering the context relationships among the features of a pair of miRNA and disease nodes, a context relationship sensitive transformer is constructed to integrate these relationships. Finally, since the previous encoding layer of the transformer contains more detailed features of the pairwise, we present a multiperspective residual strategy to supplement the detailed features to the following encoding layer from the channel perspective and the feature one, respectively. The extensive experiments confirmed that SFPred outperforms eight state-of-the-art methods for the prediction of miRNA-disease associations, and the ablation experiments validate the effectiveness of the proposed innovations. The recall rates for the top-ranked candidate miRNAs related to the diseases and the case studies on three diseases indicate SFPred's ability in screening the reliable candidates for subsequent biological experiments.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044937","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":"MMSG-DTA: A Multimodal, Multiscale Model Based on Sequence and Graph Modalities for Drug-Target Affinity Prediction.","authors":"Jiahao Xu, Lei Ci, Bo Zhu, Guanhua Zhang, Linhua Jiang, Shixin Ye-Lehmann, Wei Long","doi":"10.1021/acs.jcim.4c01828","DOIUrl":"10.1021/acs.jcim.4c01828","url":null,"abstract":"<p><p>Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins. These shortcomings impede the development of models capable of fully elucidating the mechanisms underlying drug-target interactions. To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs. Additionally, a graph-based modality is employed to improve the extraction of protein features from amino acid sequences. To further enhance the model's performance, an attention-based feature fusion module is incorporated to integrate diverse feature types, thereby strengthening its representation capacity and robustness. We evaluated MMSG-DTA on three public benchmark data sets─Davis, KIBA, and Metz─and the experimental results demonstrate that the proposed model outperforms several state-of-the-art methods in DTA prediction. These findings highlight the effectiveness of MMSG-DTA in advancing the accuracy and robustness of drug-target interaction modeling.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"981-996"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941366","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":"mTOR Variants Activation Discovers PI3K-like Cryptic Pocket, Expanding Allosteric, Mutant-Selective Inhibitor Designs.","authors":"Yonglan Liu, Wengang Zhang, Hyunbum Jang, Ruth Nussinov","doi":"10.1021/acs.jcim.4c02022","DOIUrl":"10.1021/acs.jcim.4c02022","url":null,"abstract":"<p><p>mTOR plays a crucial role in PI3K/AKT/mTOR signaling. We hypothesized that mTOR activation mechanisms driving oncogenesis can advise effective therapeutic designs. To test this, we combined cancer genomic analysis with extensive molecular dynamics simulations of mTOR oncogenic variants. We observed that conformational changes within mTOR kinase domain are associated with multiple mutational activation events. The mutations disturb the α-packing formed by the kαAL, kα3, kα9, kα9b, and kα10 helices in the kinase domain, creating cryptic pocket. Its opening correlates with opening of the catalytic cleft, including active site residues realignment, favoring catalysis. The cryptic pocket created by disrupted α-packing coincides with the allosteric pocket in PI3Kα can be harmoniously fitted by the PI3Kα allosteric inhibitor RLY-2608, suggesting that analogous drugs designed based on RLY-2608 can restore the packed α-structure, resulting in mTOR inactive conformation. Our results exemplify that knowledge of detailed kinase activation mechanisms can inform innovative allosteric inhibitor development.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"966-980"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941367","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":"Modeling and Elucidating the Behavior of a Thermoresponsive LCST Ionic Liquid.","authors":"Hussen O Mohammed, Abel de Cozar, Ronen Zangi","doi":"10.1021/acs.jcim.4c02036","DOIUrl":"10.1021/acs.jcim.4c02036","url":null,"abstract":"<p><p>Desalination of seawater by forward osmosis is a technology potentially able to address the global water scarcity problem. The major challenge limiting its widespread practical application is the design of a draw solute that can be separated from water by an energetically efficient process and then reused for the next cycle. Recent experiments demonstrate that a promising draw solute for forward-osmosis desalination is tetrabutylphosphonium 2,4,6-trimethylbenzenesulfonate ([P₄₄₄₄][TMBS]). When mixed with water, this ionic liquid (IL) is thermoresponsive and exhibits a lower critical solution temperature (LCST), above which it phase-separates into an IL-rich phase and a water-rich phase. Elucidating the physical mechanism of the liquid-liquid phase separation, as well as rationally designing optimized derivatives, necessitates an accurate model to describe this and related ILs. In this paper, we resort to explicit-solvent all-atom molecular dynamics simulations and adopt AMBER-based force-field parameters for the cation whose partial charges were assigned by the RESP fitting procedure. Utilizing the same methodology, we parametrize the anion. The simulations' results indicate the IL/water mixture, at the experimental critical composition, can unambiguously phase-separate only when the partial charges of the ions are scaled down. Nevertheless, the best-performing charge scaling factor is found to be 0.95, a value much milder than those reported for ILs in neat phases. This can be explained by a diminished charge transfer, or induced dipoles, within the ions when the IL is in a mixture with water. With this charge scaling, the simulations reproduce well the LCST composition-temperature phase diagram, albeit overestimation of the critical temperature by 10 K. In particular, very good agreement is obtained for the composition of the two segregated phases. Estimation of viscosity points to IL/water mixture that is almost twice as viscous in simulations than that reported experimentally. Furthermore, we analyze changes in energy between different components in the mixture and find that the driving force for phase separation is, at least, enthalpic. Structural analyses of the ions and their interactions with water molecules corroborate the importance of the latter in mediating structural organizations of the anions, as well as in strengthening the interactions between the cations.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"785-797"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142963250","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":"Thermal Transport through CTAB- and MTAB-Functionalized Gold Interfaces Using Molecular Dynamics Simulations.","authors":"Sydney A Shavalier, J Daniel Gezelter","doi":"10.1021/acs.jcim.4c02195","DOIUrl":"10.1021/acs.jcim.4c02195","url":null,"abstract":"<p><p>Thermal transport coefficients, notably the interfacial thermal conductance, were determined in planar and spherical gold interfaces functionalized with CTAB (cetyltrimethylammonium bromide) or MTAB (16-mercapto-hexadecyl-trimethylammonium bromide) using reverse nonequilibrium molecular dynamics (RNEMD) methods. The systems of interest included (111), (110), and (100) planar facets as well as nanospheres (<i>r</i> = 10 Å). The effect of metal polarizability was investigated through the implementation of the density-readjusted embedded atom model (DR-EAM), a polarizable metal potential. We find that conductance is higher in MTAB-capped interfaces, due in large part to the metal-to-ligand coupling provided by the Au-S bond. Alternatively, CTAB does not couple strongly with either the metal or the solvent, and it is largely a barrier to heat transfer, resulting in a much lower interfacial thermal conductance. Through analysis of physical contact between the ligand and the solvent, we find that there is significantly more overlap in the MTAB systems than the CTAB systems, mirroring the trends we observed in the conductance.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"811-824"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976872","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}