Pub Date : 2026-01-29DOI: 10.1021/acs.jcim.5c02639
Baodi Liu,Zhaoyang Chen,Nianlu Li,Na Li,Wenhui Zhang,Yan Li,Xin Huang,Meng Li,Xiao Li
Chemical neurotoxicity remains a critical safety concern in the domains of drug development and environmental risk assessment. In these contexts, reliable early stage prediction can significantly reduce experimental costs. In this study, we developed NeuroTDPi, a multilayer fully connected deep neural network model designed to identify neurotoxic compounds. The model employs a multimodal fusion strategy, integrating molecular characterization with feature representations tailored to three specific neurotoxicity end points: blood-brain barrier permeability, neuronal toxicity, and mammalian neurotoxicity. In order to enhance the interpretability of the model, the SHapley Additive Explanations (SHAP) method was employed to elucidate the contributions of various physical and chemical properties. NeuroTDPi exhibited a commendable performance, attaining area under the receiver operating characteristic curve values of 0.97, 0.84, and 0.82 for the three end points, respectively. Furthermore, a comprehensive mining and visualization workflow identified structural alerts associated with neurotoxicity, offering mechanistic insights into the observed toxic effects. These resources, which provide a robust platform for neurotoxicity evaluation and actionable structural insights for risk assessment, are freely available at https://www.sapredictor.cn/.
{"title":"NeuroTDPi: Interpretable Deep Learning Models with Multimodal Fusion for Identifying Neurotoxic Compounds.","authors":"Baodi Liu,Zhaoyang Chen,Nianlu Li,Na Li,Wenhui Zhang,Yan Li,Xin Huang,Meng Li,Xiao Li","doi":"10.1021/acs.jcim.5c02639","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02639","url":null,"abstract":"Chemical neurotoxicity remains a critical safety concern in the domains of drug development and environmental risk assessment. In these contexts, reliable early stage prediction can significantly reduce experimental costs. In this study, we developed NeuroTDPi, a multilayer fully connected deep neural network model designed to identify neurotoxic compounds. The model employs a multimodal fusion strategy, integrating molecular characterization with feature representations tailored to three specific neurotoxicity end points: blood-brain barrier permeability, neuronal toxicity, and mammalian neurotoxicity. In order to enhance the interpretability of the model, the SHapley Additive Explanations (SHAP) method was employed to elucidate the contributions of various physical and chemical properties. NeuroTDPi exhibited a commendable performance, attaining area under the receiver operating characteristic curve values of 0.97, 0.84, and 0.82 for the three end points, respectively. Furthermore, a comprehensive mining and visualization workflow identified structural alerts associated with neurotoxicity, offering mechanistic insights into the observed toxic effects. These resources, which provide a robust platform for neurotoxicity evaluation and actionable structural insights for risk assessment, are freely available at https://www.sapredictor.cn/.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"42 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073279","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}
Pub Date : 2026-01-28DOI: 10.1021/acs.jcim.5c02951
Hong Li,Yongjun Liu,Yongqing Zhang
The multidomain metalloenzyme SznF can specifically catalyze the conversion of Nω-methyl-l-arginine (l-NMA) to Nδ-hydroxy-Nω-methyl-Nω-nitroso-l-citrulline (l-NHMA), which is the key step for the biosynthesis of the N-nitrosourea pharmacophore, a precursor to the pancreatic cancer drug streptozotocin (SZN). The central domain of SznF is responsible for mediating the two sequential hydroxylations of l-NMA at Nδ and Nω positions to first generate Nδ,Nω-dihydroxy-Nω-methyl-l-arginine (l-DHMA), and the cupin domain of SznF promotes the N-migration and oxidative rearrangement of l-DHMA. This structural rearrangement contains both the C═N bond cleavage and N-N bond formation, and it is very challenging for chemical synthesis. To illuminate the catalytic mechanism of the cupin domain of SznF, we constructed the reactant models and performed a series of QM/MM calculations. We first determined the protonated states of two hydroxyls and imino of l-DHMA by calculating their pKa values, which are considered to be a crucial factor for theoretically exploring the reaction rhythm. The estimated pKa values revealed that the two hydroxyls and imino of l-DHMA should be in protonated states, and the previously proposed reaction mechanism in which superoxo addition to the unsaturated carbon as the first step is unlikely. Instead, the FeII-O2•- unit should first abstract a hydrogen from the Nω-hydroxyl group to trigger the reaction, and then the generated FeIII-OOH attacks the unsaturated carbon to form the peroxide-bridged intermediate, followed by the concerted O-O and N-C bond cleavage leading to the formation of the Fe-coordinated NO radical, which is the precondition for N-migration. During the reaction, the iron ion plays important roles, not only as a central ion to coordinate with the substrate to mediate the H-abstraction, Fe-OOH attack as well as the bond cleavage and formation but also in stabilizing the NO radical and promoting the final N-N bond formation. These results may deepen the understanding of the catalysis of nonheme iron enzymes.
{"title":"Computational Insights into the N-Migration and Oxidative Rearrangement Involved in the N-Nitrosourea Formation Catalyzed by the Cupin Domain of Multidomain Metalloenzyme SznF.","authors":"Hong Li,Yongjun Liu,Yongqing Zhang","doi":"10.1021/acs.jcim.5c02951","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02951","url":null,"abstract":"The multidomain metalloenzyme SznF can specifically catalyze the conversion of Nω-methyl-l-arginine (l-NMA) to Nδ-hydroxy-Nω-methyl-Nω-nitroso-l-citrulline (l-NHMA), which is the key step for the biosynthesis of the N-nitrosourea pharmacophore, a precursor to the pancreatic cancer drug streptozotocin (SZN). The central domain of SznF is responsible for mediating the two sequential hydroxylations of l-NMA at Nδ and Nω positions to first generate Nδ,Nω-dihydroxy-Nω-methyl-l-arginine (l-DHMA), and the cupin domain of SznF promotes the N-migration and oxidative rearrangement of l-DHMA. This structural rearrangement contains both the C═N bond cleavage and N-N bond formation, and it is very challenging for chemical synthesis. To illuminate the catalytic mechanism of the cupin domain of SznF, we constructed the reactant models and performed a series of QM/MM calculations. We first determined the protonated states of two hydroxyls and imino of l-DHMA by calculating their pKa values, which are considered to be a crucial factor for theoretically exploring the reaction rhythm. The estimated pKa values revealed that the two hydroxyls and imino of l-DHMA should be in protonated states, and the previously proposed reaction mechanism in which superoxo addition to the unsaturated carbon as the first step is unlikely. Instead, the FeII-O2•- unit should first abstract a hydrogen from the Nω-hydroxyl group to trigger the reaction, and then the generated FeIII-OOH attacks the unsaturated carbon to form the peroxide-bridged intermediate, followed by the concerted O-O and N-C bond cleavage leading to the formation of the Fe-coordinated NO radical, which is the precondition for N-migration. During the reaction, the iron ion plays important roles, not only as a central ion to coordinate with the substrate to mediate the H-abstraction, Fe-OOH attack as well as the bond cleavage and formation but also in stabilizing the NO radical and promoting the final N-N bond formation. These results may deepen the understanding of the catalysis of nonheme iron enzymes.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069944","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}
Voltage-gated sodium channels (VGSCs/Navs) are essential targets for the treatment of numerous neurological, muscular, and cardiac disorders. Despite the increasing clinical interest in subtype-selective modulators, current public databases provide fragmented and inconsistent information on VGSC-related compounds and targets, particularly lacking coverage on peptides. To address this limitation, we developed NavDB, a specialized and open-access database focusing on VGSC modulators and targets. NavDB integrates 8023 curated data records covering 5168 compounds, including small molecules, toxins, drugs, and peptides, along with comprehensive annotations on biological activity, druggability, and structural feature. NavDB also features advanced functions such as text-based and structure-based search, peptide similarity matching, and AI-powered property prediction. Moreover, the database offers high-quality 3D visualizations of targets and peptides, with disulfide bond and signal peptide annotations. All data are freely downloadable to support both experimental and computational drug discovery. NavDB is publicly available at: http://cadd.zju.edu.cn/navdb/.
{"title":"NavDB: A Comprehensive Database for Voltage-Gated Sodium Channels Modulators and Targets.","authors":"Gaoang Wang,Jiahui Yu,Haiyi Chen,Hao Luo,Peichen Pan,Tingjun Hou","doi":"10.1021/acs.jcim.5c02124","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02124","url":null,"abstract":"Voltage-gated sodium channels (VGSCs/Navs) are essential targets for the treatment of numerous neurological, muscular, and cardiac disorders. Despite the increasing clinical interest in subtype-selective modulators, current public databases provide fragmented and inconsistent information on VGSC-related compounds and targets, particularly lacking coverage on peptides. To address this limitation, we developed NavDB, a specialized and open-access database focusing on VGSC modulators and targets. NavDB integrates 8023 curated data records covering 5168 compounds, including small molecules, toxins, drugs, and peptides, along with comprehensive annotations on biological activity, druggability, and structural feature. NavDB also features advanced functions such as text-based and structure-based search, peptide similarity matching, and AI-powered property prediction. Moreover, the database offers high-quality 3D visualizations of targets and peptides, with disulfide bond and signal peptide annotations. All data are freely downloadable to support both experimental and computational drug discovery. NavDB is publicly available at: http://cadd.zju.edu.cn/navdb/.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"87 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056924","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}
Pub Date : 2026-01-28DOI: 10.1021/acs.jcim.5c02421
Hector Medina,Rachel Drake
Using machine learning to accelerate the characterization and prediction of properties of many-molecule systems, such as polymers, is appealing, yet challenging. Polymers are large, complex molecules that have unique properties and potential applications in a wide range of industries. Their potential in advancing fields such as ion-transport polymer for energy storage, lightweighting of structural materials, bioinspired multifunctional materials, etc., provide enough impetus for accelerating the discovery of novel polymeric materials. However, mathematical mapping and the consequent manipulation of polymer structures are still challenging tasks due to their complex configuration and the smorgasbord of motifs encountered naturally and in engineering materials. Traditional methods of polymer structure mapping and property prediction at multiscale domains can include approaches such as Density Functional Theory, Molecular Dynamics, and Finite Element Analysis, which can be time-consuming and computationally expensive. The promise of machine learning to accelerate these tasks is appealing, and currently, researchers are pursuing the development of architectures and composition approaches to accomplish this. Here we discuss the current state of the knowledge on the use of Graph Neural Networks, and related architectures, being developed and/or used for the characterization and prediction of properties of polymers. Many challenges still exist such as the lack of sufficient and comprehensive data sets. To address these issues, efforts are being pursued─such as the so-called CRIPT (Community Resource for Innovation in Polymer Technology) led by a lab consortium that includes representations from private industry, academia, government, and others. We conclude that even though this field is young it has both momentum and promise. The current challenges that must be overcome are also addressed.
使用机器学习来加速表征和预测许多分子系统（如聚合物）的性质，是有吸引力的，但也是具有挑战性的。聚合物是大而复杂的分子，具有独特的性质，在广泛的工业中具有潜在的应用。它们在离子传输聚合物储能、结构材料轻量化、仿生多功能材料等领域的发展潜力，为加速新型聚合物材料的发现提供了足够的动力。然而，由于聚合物结构的复杂结构和自然和工程材料中遇到的自助餐式图案，数学映射和随后的聚合物结构操作仍然是一项具有挑战性的任务。传统的多尺度聚合物结构作图和性质预测方法包括密度泛函理论、分子动力学和有限元分析等方法，这些方法耗时且计算成本高。机器学习加速这些任务的承诺很有吸引力，目前，研究人员正在追求架构和组合方法的开发来实现这一目标。在这里，我们讨论了关于使用图神经网络的知识的现状，以及相关的架构，正在开发和/或用于表征和预测聚合物的性质。许多挑战仍然存在，例如缺乏充分和全面的数据集。为了解决这些问题，人们正在努力，比如由一个实验室联盟领导的所谓的“聚合物技术创新社区资源”（Community Resource for Innovation in Polymer Technology），该联盟包括来自私营企业、学术界、政府和其他方面的代表。我们的结论是，尽管这个领域还很年轻，但它既有势头，也有希望。还讨论了当前必须克服的挑战。
{"title":"Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.","authors":"Hector Medina,Rachel Drake","doi":"10.1021/acs.jcim.5c02421","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02421","url":null,"abstract":"Using machine learning to accelerate the characterization and prediction of properties of many-molecule systems, such as polymers, is appealing, yet challenging. Polymers are large, complex molecules that have unique properties and potential applications in a wide range of industries. Their potential in advancing fields such as ion-transport polymer for energy storage, lightweighting of structural materials, bioinspired multifunctional materials, etc., provide enough impetus for accelerating the discovery of novel polymeric materials. However, mathematical mapping and the consequent manipulation of polymer structures are still challenging tasks due to their complex configuration and the smorgasbord of motifs encountered naturally and in engineering materials. Traditional methods of polymer structure mapping and property prediction at multiscale domains can include approaches such as Density Functional Theory, Molecular Dynamics, and Finite Element Analysis, which can be time-consuming and computationally expensive. The promise of machine learning to accelerate these tasks is appealing, and currently, researchers are pursuing the development of architectures and composition approaches to accomplish this. Here we discuss the current state of the knowledge on the use of Graph Neural Networks, and related architectures, being developed and/or used for the characterization and prediction of properties of polymers. Many challenges still exist such as the lack of sufficient and comprehensive data sets. To address these issues, efforts are being pursued─such as the so-called CRIPT (Community Resource for Innovation in Polymer Technology) led by a lab consortium that includes representations from private industry, academia, government, and others. We conclude that even though this field is young it has both momentum and promise. The current challenges that must be overcome are also addressed.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"93 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070071","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}
Pub Date : 2026-01-28DOI: 10.1021/acs.jcim.5c02227
Pablo Navarro Acero,Ming-Hong Hao,Karan Kapoor
The tumor suppressor p53 regulates transcription in response to cellular stress, with mutations in its DNA-binding domain (DBD) found in most human cancers. The L1 loop within the DBD is believed to play a critical role in DNA recognition, yet its conformational dynamics remain poorly understood. Using enhanced molecular dynamics simulations combined with machine learning-derived collective variables, we reveal a novel conformational switch mechanism governing p53's DNA-binding activity. Our analysis identifies two distinct transition pathways between extended (DNA-binding competent) and recessed conformations, each characterized by specific hydrogen bond networks and high energy barriers. We discovered a potential allosteric mechanism regulating the DNA-p53 binding interface that could provide an atomistic basis for gene-specific transcription regulation. This mechanism would explain the prevalence of certain cancer mutations, particularly at residue R282. Finally, we provide a mechanistic rationale for how compounds targeting a reactivation pocket near the L1 loop may restore p53 function by modulating DNA binding kinetics rather than affinity, thereby reconciling previously observed rescue effects.
{"title":"AI-Guided Conformational Dynamics of p53 L1 Loop Reveal an Allosteric Switch Regulating DNA Binding and Cancer Hotspots.","authors":"Pablo Navarro Acero,Ming-Hong Hao,Karan Kapoor","doi":"10.1021/acs.jcim.5c02227","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02227","url":null,"abstract":"The tumor suppressor p53 regulates transcription in response to cellular stress, with mutations in its DNA-binding domain (DBD) found in most human cancers. The L1 loop within the DBD is believed to play a critical role in DNA recognition, yet its conformational dynamics remain poorly understood. Using enhanced molecular dynamics simulations combined with machine learning-derived collective variables, we reveal a novel conformational switch mechanism governing p53's DNA-binding activity. Our analysis identifies two distinct transition pathways between extended (DNA-binding competent) and recessed conformations, each characterized by specific hydrogen bond networks and high energy barriers. We discovered a potential allosteric mechanism regulating the DNA-p53 binding interface that could provide an atomistic basis for gene-specific transcription regulation. This mechanism would explain the prevalence of certain cancer mutations, particularly at residue R282. Finally, we provide a mechanistic rationale for how compounds targeting a reactivation pocket near the L1 loop may restore p53 function by modulating DNA binding kinetics rather than affinity, thereby reconciling previously observed rescue effects.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"36 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070072","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}
Molecular property prediction refers to predicting the properties of a given molecular representation. This task is of great significance in fields such as drug design and has garnered widespread attention from researchers. For molecular property prediction, the quality of feature learning plays a decisive role in model performance. Although existing molecular graph models can extract effective feature representations from graph structures, how to better utilize these features across different learning tasks remains an important challenge. This paper proposes a subgraph-optimized Graph Autoencoder (TurboGAE) and several multimodal fusion strategies. By introducing a subgraph-level graph tokenizer, TurboGAE more effectively captures the impact of substructure features (within molecular structures) on molecular properties. For cross-modal molecular features, a rational and effective multimodal feature fusion strategy can align intermodal features during the pretraining phase, leveraging the unique strengths of each modality. The proposed methods demonstrate excellent performance in experiments on downstream tasks.
{"title":"Multi-Modal Fusion Frameworks of Subgraph-Optimized Graph Autoencoder for Molecular Property Prediction.","authors":"Kaiyuan Zhang,Congyu Han,Fenghua Zhang,Cheng Lin,Quanlong Li,Tianyi Zang,Yanli Zhao","doi":"10.1021/acs.jcim.5c02536","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02536","url":null,"abstract":"Molecular property prediction refers to predicting the properties of a given molecular representation. This task is of great significance in fields such as drug design and has garnered widespread attention from researchers. For molecular property prediction, the quality of feature learning plays a decisive role in model performance. Although existing molecular graph models can extract effective feature representations from graph structures, how to better utilize these features across different learning tasks remains an important challenge. This paper proposes a subgraph-optimized Graph Autoencoder (TurboGAE) and several multimodal fusion strategies. By introducing a subgraph-level graph tokenizer, TurboGAE more effectively captures the impact of substructure features (within molecular structures) on molecular properties. For cross-modal molecular features, a rational and effective multimodal feature fusion strategy can align intermodal features during the pretraining phase, leveraging the unique strengths of each modality. The proposed methods demonstrate excellent performance in experiments on downstream tasks.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"87 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056732","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}
Pub Date : 2026-01-27DOI: 10.1021/acs.jcim.5c01536
Elif Naz Bingol,Pemra Ozbek
Allostery, a crucial phenomenon for comprehending protein function, interactions, and regulation, involves the transmission of perturbations induced by ligand binding to distant sites within a molecule. Understanding the mechanisms of allostery holds the key to elucidating signal transmission failures and diseases resulting from such disruptions. This study focuses on contributing to this understanding by delving into the intricate dynamics of T-cell receptor and peptide-major histocompatibility complex interactions, which are essential components in the communication network of biological systems. Aiming to reveal the effect of melanoma-associated peptides on allosteric signaling, valuable insights are provided. Molecular dynamics simulations were performed on melanoma-associated-epitope-bound TCR-pMHC complexes, followed by machine learning clustering and network analysis, where this innovative combination facilitated the identification of critical TCR-pMHC contacts that modulate global dynamics and stability, presenting novel insights into the complex dynamics of TCR-pMHC interactions. The results not only contributed molecular understanding to TCR-pMHC interactions but also offered valuable information for the fields of immunotherapy and protein engineering. The findings serve as a guide for future experimental investigations and advance our understanding of the immune response in the context of melanoma.
{"title":"Allosteric Insights into TCR-pMHC Dynamics: Understanding the Effects of Melanoma-Associated Epitopes.","authors":"Elif Naz Bingol,Pemra Ozbek","doi":"10.1021/acs.jcim.5c01536","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01536","url":null,"abstract":"Allostery, a crucial phenomenon for comprehending protein function, interactions, and regulation, involves the transmission of perturbations induced by ligand binding to distant sites within a molecule. Understanding the mechanisms of allostery holds the key to elucidating signal transmission failures and diseases resulting from such disruptions. This study focuses on contributing to this understanding by delving into the intricate dynamics of T-cell receptor and peptide-major histocompatibility complex interactions, which are essential components in the communication network of biological systems. Aiming to reveal the effect of melanoma-associated peptides on allosteric signaling, valuable insights are provided. Molecular dynamics simulations were performed on melanoma-associated-epitope-bound TCR-pMHC complexes, followed by machine learning clustering and network analysis, where this innovative combination facilitated the identification of critical TCR-pMHC contacts that modulate global dynamics and stability, presenting novel insights into the complex dynamics of TCR-pMHC interactions. The results not only contributed molecular understanding to TCR-pMHC interactions but also offered valuable information for the fields of immunotherapy and protein engineering. The findings serve as a guide for future experimental investigations and advance our understanding of the immune response in the context of melanoma.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"293 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056736","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}
Pub Date : 2026-01-27DOI: 10.1021/acs.jcim.5c02839
Yan Wang,Yunzhi Liu,Chenxu Si,Jie Hong,Lei Wang,Lan Huang,Nan Sheng
MicroRNAs (miRNAs) play critical roles in regulating various biological processes and offer significant potential for treating human diseases. Aberrant expression of miRNAs is known to contribute to drug resistance/sensitivity, posing a significant challenge to miRNA-based therapeutic approaches. Currently, traditional biological experiments to detect miRNA-drug associations (MDAs) are costly and time-consuming, while sequence- or topology-based deep learning methods have gained recognition for their efficiency and accuracy. Nevertheless, existing computational methods tend to ignore multiple sources of information and are overly reliant on known MDAs. We introduce an attention-guided multiview deep learning framework (DLMVF) for predicting MDAs. Our innovative approach fully integrates multisource information about miRNAs and drugs rather than relying exclusively on interaction graph data. DLMVF contains miRNA attribute view encoder, drug attribute view encoder, and miRNA-drug interactions encoder modules, enabling the extraction of miRNA and drug features from multiple perspectives. Moreover, the DLMVF can enhance the learned latent representations for association prediction through view-level attention, which adaptively learns the importance of different features. To evaluate the effectiveness of DLMVF, we manually constructed an experimental benchmark data set based on the latest database. DLMVF achieves an AUROC of 0.9611 and an AUPRC of 0.9543 on the benchmark data set. Extensive benchmarking demonstrates that the DLMVF outperforms existing methods with good robustness and generalization. In addition, a case study of three common anticancer drugs demonstrates its effectiveness in discovering novel MDAs. Data and source code will be published at https://github.com/Lgubig/DLMVF_model.
MicroRNAs （miRNAs）在调节各种生物过程中发挥着关键作用，并为治疗人类疾病提供了巨大的潜力。已知mirna的异常表达有助于耐药/敏感性，这对基于mirna的治疗方法提出了重大挑战。目前，传统的检测miRNA-drug association （mda）的生物学实验既昂贵又耗时，而基于序列或拓扑的深度学习方法因其效率和准确性而获得认可。然而，现有的计算方法往往忽略了多个信息来源，并过度依赖于已知的mda。我们引入了一个注意力引导的多视图深度学习框架（DLMVF）来预测mda。我们的创新方法完全整合了有关mirna和药物的多源信息，而不仅仅依赖于相互作用图数据。DLMVF包含miRNA属性视图编码器、药物属性视图编码器和miRNA-药物相互作用编码器模块，可多角度提取miRNA和药物特征。此外，DLMVF还可以通过自适应学习不同特征的重要性，增强学习到的潜在关联表征。为了评估DLMVF的有效性，我们基于最新的数据库手动构建了一个实验基准数据集。DLMVF在基准数据集上的AUROC为0.9611，AUPRC为0.9543。大量的基准测试表明，DLMVF具有良好的鲁棒性和泛化性，优于现有的方法。此外，对三种常见抗癌药物的案例研究证明了其在发现新型mda方面的有效性。数据和源代码将在https://github.com/Lgubig/DLMVF_model上发布。
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Pub Date : 2026-01-27DOI: 10.1021/acs.jcim.5c02384
Jesko Kaiser,Christoph G W Gertzen,Daniel Mann,Carsten Sachse,Holger Gohlke
The homopentameric α7-nicotinic acetylcholine receptor (nAChR) is a ligand-gated ion channel widely expressed in the human nervous system and susceptible to allosteric modulation. A recent cryo-EM structure (EMD 22983; PDB ID 7KOX) revealed unassigned Coulomb density. Unbiased molecular dynamics simulations of buffer components around α7-nAChR show that (±)-epibatidine can occupy not only the orthosteric site but also the pore near the desensitization gate, consistent with the unmodeled Coulomb density and expanding the receptor's pocketome.
同戊二聚α7-烟碱乙酰胆碱受体（nAChR）是人类神经系统中广泛表达的一种配体门控离子通道，易受变构调节。最近的低温电镜结构（EMD 22983; PDB ID 7KOX）显示未分配的库仑密度。α7-nAChR周围缓冲组分的无偏分子动力学模拟表明，（±）-epibatidine不仅可以占据正构位，还可以占据脱敏门附近的孔，这与未建模的库仑密度一致，并扩大了受体的囊袋。
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We present a framework for data-efficient training of machine-learning interatomic potentials for interfacial chemistry, especially heterogeneous catalytic systems. We establish strategies for density functional theory training data generation consisting of procedurally generated bulk, surface, and gas-phase atomic geometries, as well as moderately randomized structures. We show how ensembles of neural network machine-learning interatomic potentials trained on different splits of these training structures yield reliable uncertainty estimates at the atomic node energy level. Our models can thus identify which atomic sites and chemical bonds in a system lead to uncertainties in the predicted potential energy surface. Using hydrogen interacting with platinum as a test case, we find that the atomic uncertainty estimates identify both unphysical bonding scenarios and physically relevant interactions that are underrepresented in the original training data, such as surface diffusion, bond breaking, and bond formation. Building on these insights, we propose local uncertainty-informed strategies that flag outliers via statistical correlations, thereby improving active learning efficiency and enhancing the reliability of neural network-based potentials for extended-scale reactive dynamics.
{"title":"Optimizing Prediction of Chemical Bonds in Interfacial Dynamics through Local Uncertainty Estimates with Neural Network Ensembles.","authors":"Suman Bhasker-Ranganath,Filippo Balzaretti,Johannes Voss","doi":"10.1021/acs.jcim.5c02083","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02083","url":null,"abstract":"We present a framework for data-efficient training of machine-learning interatomic potentials for interfacial chemistry, especially heterogeneous catalytic systems. We establish strategies for density functional theory training data generation consisting of procedurally generated bulk, surface, and gas-phase atomic geometries, as well as moderately randomized structures. We show how ensembles of neural network machine-learning interatomic potentials trained on different splits of these training structures yield reliable uncertainty estimates at the atomic node energy level. Our models can thus identify which atomic sites and chemical bonds in a system lead to uncertainties in the predicted potential energy surface. Using hydrogen interacting with platinum as a test case, we find that the atomic uncertainty estimates identify both unphysical bonding scenarios and physically relevant interactions that are underrepresented in the original training data, such as surface diffusion, bond breaking, and bond formation. Building on these insights, we propose local uncertainty-informed strategies that flag outliers via statistical correlations, thereby improving active learning efficiency and enhancing the reliability of neural network-based potentials for extended-scale reactive dynamics.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"73 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056737","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}
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