Pub Date : 2026-03-13DOI: 10.1021/acs.jcim.5c03091
Ana-Maria Caldaruse, Hannah M Baumann, David L Mobley
Fragment-based drug discovery (FBDD) is a widely used strategy in early-stage drug development, but accurately predicting the binding affinities of fragments and their elaborated analogs poses unique computational challenges. These difficulties arise from weak binding affinities, diverse chemical scaffolds, and limited structural overlap between fragments and their optimized derivatives. While several free-energy methods exist, few are tailored to the specific requirements of FBDD. In this study, we evaluate the Separated Topologies (SepTop) approach for modeling fragment-based transformations, including fragment merging and linking. Using retrospective data sets from Cyclophilin D and SARS-CoV-2 Macrodomain 1, we demonstrate that SepTop can recover experimental binding affinities with good accuracy across both fragment and lead-like compounds. These results support SepTop's suitability for fragment optimization and highlight its potential to extend the reach of binding free-energy calculations into earlier stages of drug discovery.
{"title":"Efficient Binding Affinity Estimation for Fragment-Based Compounds Using a Separated Topologies Approach.","authors":"Ana-Maria Caldaruse, Hannah M Baumann, David L Mobley","doi":"10.1021/acs.jcim.5c03091","DOIUrl":"10.1021/acs.jcim.5c03091","url":null,"abstract":"<p><p>Fragment-based drug discovery (FBDD) is a widely used strategy in early-stage drug development, but accurately predicting the binding affinities of fragments and their elaborated analogs poses unique computational challenges. These difficulties arise from weak binding affinities, diverse chemical scaffolds, and limited structural overlap between fragments and their optimized derivatives. While several free-energy methods exist, few are tailored to the specific requirements of FBDD. In this study, we evaluate the Separated Topologies (SepTop) approach for modeling fragment-based transformations, including fragment merging and linking. Using retrospective data sets from Cyclophilin D and SARS-CoV-2 Macrodomain 1, we demonstrate that SepTop can recover experimental binding affinities with good accuracy across both fragment and lead-like compounds. These results support SepTop's suitability for fragment optimization and highlight its potential to extend the reach of binding free-energy calculations into earlier stages of drug discovery.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454826","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 : 2026-03-12DOI: 10.1021/acs.jcim.5c03011
Zihan Zhang,Yuchen Zhang
Noncoding RNAs (ncRNAs) play critical regulatory roles in cancer drug response. However, most existing methods are limited to predicting a single type of ncRNA, failing to fully capture the complex semantic associations between multimodal biological features, and thus exhibit weak generalizability and robustness. To overcome these limitations, this study proposes NCRDLLM, a unified framework that leverages large language models (LLMs) to predict associations between three types of ncRNA (circular RNA, microRNA, and long noncoding RNA) and drugs. The method integrates 19,020 experimentally validated associations and 120,009 disease association records. Three types of multimodal features are constructed: sequence features extracted using pretrained foundation models RNA-FM and ChemBERTa, structural features generated through Graph2Vec for RNA secondary structures and AttentiveFP combined with ECFP for drug molecules, and association features obtained via disease-associated coding and semantic similarity. These features are subsequently mapped into the hidden space of LLaMA-3.2-3B through adapter modules, with LoRA employed for parameter-efficient fine-tuning. Experimental results demonstrate that NCRDLLM achieves AUC-ROC values of 0.9665, 0.9832, and 0.9676 on miRNA-drug, lncRNA-drug, and circRNA-drug data sets, respectively. Ablation studies confirm the contribution of each module, while literature evidence and tissue-specific expression profiling further support the biological relevance of the predictions. NCRDLLM provides an effective strategy for identifying potential ncRNA-drug response associations.
{"title":"NCRDLLM: Predicting ncRNA-Drug Response Associations via Multimodal Feature Fusion and Large Language Models.","authors":"Zihan Zhang,Yuchen Zhang","doi":"10.1021/acs.jcim.5c03011","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c03011","url":null,"abstract":"Noncoding RNAs (ncRNAs) play critical regulatory roles in cancer drug response. However, most existing methods are limited to predicting a single type of ncRNA, failing to fully capture the complex semantic associations between multimodal biological features, and thus exhibit weak generalizability and robustness. To overcome these limitations, this study proposes NCRDLLM, a unified framework that leverages large language models (LLMs) to predict associations between three types of ncRNA (circular RNA, microRNA, and long noncoding RNA) and drugs. The method integrates 19,020 experimentally validated associations and 120,009 disease association records. Three types of multimodal features are constructed: sequence features extracted using pretrained foundation models RNA-FM and ChemBERTa, structural features generated through Graph2Vec for RNA secondary structures and AttentiveFP combined with ECFP for drug molecules, and association features obtained via disease-associated coding and semantic similarity. These features are subsequently mapped into the hidden space of LLaMA-3.2-3B through adapter modules, with LoRA employed for parameter-efficient fine-tuning. Experimental results demonstrate that NCRDLLM achieves AUC-ROC values of 0.9665, 0.9832, and 0.9676 on miRNA-drug, lncRNA-drug, and circRNA-drug data sets, respectively. Ablation studies confirm the contribution of each module, while literature evidence and tissue-specific expression profiling further support the biological relevance of the predictions. NCRDLLM provides an effective strategy for identifying potential ncRNA-drug response associations.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"11 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439437","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 : 2026-03-12DOI: 10.1021/acs.jcim.5c02999
Gabriel Rodríguez-Santos,Giorgio Bonollo,Cristiano Sciva,Giorgio Colombo,Concepción Pérez-Melero,Stefano A Serapian
Human kinesin-5 is a protein that oversees the proper formation of the bipolar mitotic spindle and is thus an appealing target for cancer treatment. The main group of kinesin-5 inhibitors reported to date binds to an allosteric pocket formed by loop 5 (L5), which is a key structural element believed to allosterically modulate kinesin-5 functionality. In this study, we carried out extensive molecular dynamics (MD) simulations on the motor domain of kinesin-5 in four representative catalytic states: ATP-bound, ADP-bound, without nucleotide (apo), and dually bound by ADP and the known main group inhibitor filanesib. MD trajectories were analyzed using the Distance Fluctuation and Shortest Path Map methods to compare and contrast allosteric connections across different parts of the motor domain in each of the four states. Simulations show that L5 is allosterically connected to both the nucleotide-binding site and the kinesin-5-microtubule interface. In the absence of inhibitor, L5 alternates between a "docked" conformation in the ATP and apo states and an "undocked" conformation in the ADP state. This supports the idea that the L5 binding pocket is cryptic and that inhibitor binding takes place in the ADP state. Residues Trp127 and Tyr211 were found to be crucial for the L5 conformational alternation. Once filanesib binds to the ADP form, we found that L5 stabilizes into an ATP-like conformation that prevents ADP release, possibly via sequestration of Glu118 by filanesib itself. Additionally, the presence of filanesib intensifies anomalous allosteric connections with L8, which is a crucial mediator of microtubule binding. This could explain the low affinity of kinesin-5 for the microtubule when L5 inhibitors are present. Our findings allow a deeper understanding of the key role of L5 in regulating kinesin-5 activity and how L5 inhibitors can achieve its disruption.
{"title":"Molecular Dynamics Simulations Provide Further Insights into the Allosteric Regulation of the Kinesin-5 Motor Domain by Loop 5.","authors":"Gabriel Rodríguez-Santos,Giorgio Bonollo,Cristiano Sciva,Giorgio Colombo,Concepción Pérez-Melero,Stefano A Serapian","doi":"10.1021/acs.jcim.5c02999","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02999","url":null,"abstract":"Human kinesin-5 is a protein that oversees the proper formation of the bipolar mitotic spindle and is thus an appealing target for cancer treatment. The main group of kinesin-5 inhibitors reported to date binds to an allosteric pocket formed by loop 5 (L5), which is a key structural element believed to allosterically modulate kinesin-5 functionality. In this study, we carried out extensive molecular dynamics (MD) simulations on the motor domain of kinesin-5 in four representative catalytic states: ATP-bound, ADP-bound, without nucleotide (apo), and dually bound by ADP and the known main group inhibitor filanesib. MD trajectories were analyzed using the Distance Fluctuation and Shortest Path Map methods to compare and contrast allosteric connections across different parts of the motor domain in each of the four states. Simulations show that L5 is allosterically connected to both the nucleotide-binding site and the kinesin-5-microtubule interface. In the absence of inhibitor, L5 alternates between a \"docked\" conformation in the ATP and apo states and an \"undocked\" conformation in the ADP state. This supports the idea that the L5 binding pocket is cryptic and that inhibitor binding takes place in the ADP state. Residues Trp127 and Tyr211 were found to be crucial for the L5 conformational alternation. Once filanesib binds to the ADP form, we found that L5 stabilizes into an ATP-like conformation that prevents ADP release, possibly via sequestration of Glu118 by filanesib itself. Additionally, the presence of filanesib intensifies anomalous allosteric connections with L8, which is a crucial mediator of microtubule binding. This could explain the low affinity of kinesin-5 for the microtubule when L5 inhibitors are present. Our findings allow a deeper understanding of the key role of L5 in regulating kinesin-5 activity and how L5 inhibitors can achieve its disruption.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393853","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 : 2026-03-11DOI: 10.1021/acs.jcim.5c03128
Huibin Wang,Yueqing Zhang,Zehui Wang,Jiaxi Zhuang,Zixian Cheng,Ying Qian,Aimin Zhou,Sihua Peng,Xiao He
Retrosynthesis aims to identify sets of reactants capable of synthesizing a target molecule and has recently benefited from advancements in template-free sequence-translation models, which offer both efficiency and high predictive accuracy. A challenge in this domain is effectively capturing the intrinsic one-to-many relationship characteristic of chemical reactions. To address this, we propose a Hierarchical Conditional Variational Auto-Encoder (HCVAE) module that can be seamlessly integrated into existing template-free retrosynthesis frameworks. Our method establishes a hierarchical latent space that transitions from continuous to discrete representations: a continuous latent variable explores diverse chemical transformation proposals, while a discrete latent variable groups them into high-level reaction classes. This design links one product to multiple possible reactants, thereby enhancing coverage of multicandidate synthesis schemes. Extensive evaluations conducted on three publicly available benchmarks, encompassing both single-step prediction and multistep planning tasks, demonstrate that the HCVAE consistently improves performance across various backbone architectures. For instance, the single-step RootAligned model exhibits an increase in top-10 exact match accuracy on the USPTO-50k data set from 90.5% to 91.6%, meanwhile the DirectMultistep model shows improvements from 49.3% to 53.1% and from 43.0% to 46.7% on the n1 and n5 sets of the PaRoutes data set, respectively. Further analyses indicate that the learned latent space organization provides a structured mechanism for navigating alternative reaction proposals and facilitates practical multistep synthesis of drug-like molecules.
{"title":"Enhancing Diversity of Template-Free Retrosynthesis Prediction via Hierarchical Latent Variables","authors":"Huibin Wang,Yueqing Zhang,Zehui Wang,Jiaxi Zhuang,Zixian Cheng,Ying Qian,Aimin Zhou,Sihua Peng,Xiao He","doi":"10.1021/acs.jcim.5c03128","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c03128","url":null,"abstract":"Retrosynthesis aims to identify sets of reactants capable of synthesizing a target molecule and has recently benefited from advancements in template-free sequence-translation models, which offer both efficiency and high predictive accuracy. A challenge in this domain is effectively capturing the intrinsic one-to-many relationship characteristic of chemical reactions. To address this, we propose a Hierarchical Conditional Variational Auto-Encoder (HCVAE) module that can be seamlessly integrated into existing template-free retrosynthesis frameworks. Our method establishes a hierarchical latent space that transitions from continuous to discrete representations: a continuous latent variable explores diverse chemical transformation proposals, while a discrete latent variable groups them into high-level reaction classes. This design links one product to multiple possible reactants, thereby enhancing coverage of multicandidate synthesis schemes. Extensive evaluations conducted on three publicly available benchmarks, encompassing both single-step prediction and multistep planning tasks, demonstrate that the HCVAE consistently improves performance across various backbone architectures. For instance, the single-step RootAligned model exhibits an increase in top-10 exact match accuracy on the USPTO-50k data set from 90.5% to 91.6%, meanwhile the DirectMultistep model shows improvements from 49.3% to 53.1% and from 43.0% to 46.7% on the n1 and n5 sets of the PaRoutes data set, respectively. Further analyses indicate that the learned latent space organization provides a structured mechanism for navigating alternative reaction proposals and facilitates practical multistep synthesis of drug-like molecules.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"7 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383799","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 : 2026-03-10DOI: 10.1021/acs.jcim.5c03137
Karmen Čondić-Jurkić,Irfan Alibay,Woody Sherman,Mallory R Tollefson,W Patrick Walters,Zachary Baker,Lillian T Chong,Jennifer N Wei,Jeffrey Gray,Brian D Weitzner,Daniel G A Smith,Julia Koehler Leman,Chris Bahl,David L Mobley
The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine-learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence. Additionally, it results in taxpayer-funded research being left siloed in commercial tools where it cannot have as much impact as if it were returned to the general public. This Perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort and enabling scientific validation of modeling tools and frictionless experimentation with new ideas. Coordinated multiproject consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET, have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a U.S. nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.
在基于物理和机器学习的方法的推动下，现代分子建模的重要性和预测能力日益增强，需要一种新的协作架构来取代孤立的传统软件开发模型。传统的方法通常会导致冗余的工程工作、高成本和不透明的系统，从而限制了可重复性、独立审查和科学独立性。此外，这导致纳税人资助的研究被孤立在商业工具中，无法产生与回归公众一样大的影响。这个视角提倡允许许可的开源软件作为科学和经济的乘数，通过减少重复的工作，使建模工具的科学验证和新思想的无摩擦实验成为可能。协调的多项目联盟，如Open Force Field、Open Free Energy、OpenFold和OpenADMET，已经形成协作构建共享的计算基础设施，并在许可许可下发布所有方法。这些大规模工作的成功需要超越代码的组织结构。开放分子软件基金会（OMSF），一个美国的非营利组织，作为一个特定领域的机构和财政赞助者。通过提供治理、管理基础设施和专门的研究软件工程师，OMSF将学术界和工业界利益相关者之间的激励机制统一起来。这个框架支持一个协同的生态系统，其中项目互操作加速创新，消除重复，并确保长期的软件可持续性，从而创建提升整个分子建模社区的持久基础。
{"title":"The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.","authors":"Karmen Čondić-Jurkić,Irfan Alibay,Woody Sherman,Mallory R Tollefson,W Patrick Walters,Zachary Baker,Lillian T Chong,Jennifer N Wei,Jeffrey Gray,Brian D Weitzner,Daniel G A Smith,Julia Koehler Leman,Chris Bahl,David L Mobley","doi":"10.1021/acs.jcim.5c03137","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c03137","url":null,"abstract":"The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine-learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence. Additionally, it results in taxpayer-funded research being left siloed in commercial tools where it cannot have as much impact as if it were returned to the general public. This Perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort and enabling scientific validation of modeling tools and frictionless experimentation with new ideas. Coordinated multiproject consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET, have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a U.S. nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"45 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381339","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 : 2026-03-10DOI: 10.1021/acs.jcim.5c02944
Salomón J Alas-Guardado,Melisa S Anzures-Mendoza,José Y Sol-Fragoso,Edgar López-Pérez
The primary function of the CheY protein is to regulate flagellar motility in motile bacteria such as Escherichia coli and Thermotoga maritima. Although the general determinants of thermal stability in CheY from the hyperthermophilic bacterium T. maritima (TmY) have been proposed, the molecular mechanisms that enable this protein to remain structurally and functionally competent at elevated temperatures are not fully understood. Here, we investigated the thermal stability of TmY through all-atom molecular dynamics simulations, using three independent trajectories of 1 μs each at five different temperatures. Equivalent simulations were performed for its mesophilic homologue from E. coli (EcY) to enable a direct comparison under identical conditions. Our observations show that TmY preserves its native fold and global compactness across the entire temperature range, whereas EcY exhibits progressive destabilization and unfolds at high temperatures. Mechanistically, the enhanced thermal resistance of TmY is associated with an extensive network of salt bridges that interconnect secondary-structure elements and couple the N- and C-terminal domains. These electrostatic networks act as stabilizing scaffolds that restrain local flexibility, preserve domain communication, and maintain a tightly packed globular architecture under thermal stress, providing a molecular basis for the superior stability of TmY relative to its mesophilic counterpart.
{"title":"Comparative Molecular Dynamics Study of the Thermal Stability of CheY Proteins from Hyperthermophilic and Mesophilic Organisms.","authors":"Salomón J Alas-Guardado,Melisa S Anzures-Mendoza,José Y Sol-Fragoso,Edgar López-Pérez","doi":"10.1021/acs.jcim.5c02944","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02944","url":null,"abstract":"The primary function of the CheY protein is to regulate flagellar motility in motile bacteria such as Escherichia coli and Thermotoga maritima. Although the general determinants of thermal stability in CheY from the hyperthermophilic bacterium T. maritima (TmY) have been proposed, the molecular mechanisms that enable this protein to remain structurally and functionally competent at elevated temperatures are not fully understood. Here, we investigated the thermal stability of TmY through all-atom molecular dynamics simulations, using three independent trajectories of 1 μs each at five different temperatures. Equivalent simulations were performed for its mesophilic homologue from E. coli (EcY) to enable a direct comparison under identical conditions. Our observations show that TmY preserves its native fold and global compactness across the entire temperature range, whereas EcY exhibits progressive destabilization and unfolds at high temperatures. Mechanistically, the enhanced thermal resistance of TmY is associated with an extensive network of salt bridges that interconnect secondary-structure elements and couple the N- and C-terminal domains. These electrostatic networks act as stabilizing scaffolds that restrain local flexibility, preserve domain communication, and maintain a tightly packed globular architecture under thermal stress, providing a molecular basis for the superior stability of TmY relative to its mesophilic counterpart.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"67 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383373","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 : 2026-03-09DOI: 10.1021/acs.jcim.5c02998
Måns K Rosenbaum,David van der Spoel
Molecular simulation tools, such as GROMACS, are used routinely to produce time series of energies and other observables. To turn these data into publication-quality figures, a user can either use a (commercial) software package with a graphical user interface, often offering fine control and high-quality output, or write their own code to make plots using a scripting language. In the age of big data and machine learning, it is often necessary to generate many graphs, be able to rapidly inspect them, and make plots for manuscripts. Here, we provide a simple Python tool, plotXVG, built on the well-known Matplotlib plotting library, that will generate publication-quality graphics for line graphs as well as heatmaps and contour plots. This will allow users to rapidly and reproducibly generate a series of graphics files without programming, but a simple application programming interface is available as well for incorporation in, e.g., machine learning applications. Obviously, the tool is applicable to any kind of line graph data or heatmap, not just that from molecular simulations. plotXVG is available as free and open source, which implies that users can extend the tool to their own needs.
{"title":"plotXVG: Batch Generation of Publication-Quality Graphs from GROMACS Output.","authors":"Måns K Rosenbaum,David van der Spoel","doi":"10.1021/acs.jcim.5c02998","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02998","url":null,"abstract":"Molecular simulation tools, such as GROMACS, are used routinely to produce time series of energies and other observables. To turn these data into publication-quality figures, a user can either use a (commercial) software package with a graphical user interface, often offering fine control and high-quality output, or write their own code to make plots using a scripting language. In the age of big data and machine learning, it is often necessary to generate many graphs, be able to rapidly inspect them, and make plots for manuscripts. Here, we provide a simple Python tool, plotXVG, built on the well-known Matplotlib plotting library, that will generate publication-quality graphics for line graphs as well as heatmaps and contour plots. This will allow users to rapidly and reproducibly generate a series of graphics files without programming, but a simple application programming interface is available as well for incorporation in, e.g., machine learning applications. Obviously, the tool is applicable to any kind of line graph data or heatmap, not just that from molecular simulations. plotXVG is available as free and open source, which implies that users can extend the tool to their own needs.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"193 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381340","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 : 2026-03-09DOI: 10.1021/acs.jcim.5c02826
Haotian Guan,Tian Bai,Chuande Yang,Tao Zhang,Han Wang,Guishen Wang
Accurately predicting drug-target interactions (DTIs) is crucial for drug discovery, repositioning. However, most deep learning-based DTI models are designed in Euclidean space, making it difficult to effectively represent the hierarchical and scale-free characteristics of biological data. Due to its unique negatively curved geometric properties, hyperbolic space can more effectively represent hierarchical relationships within data. Therefore, we propose a multimanifold learning framework that integrates multimodal features in hyperbolic and Euclidean spaces for drug-target interaction prediction. Specifically, we employ a Hyperbolic Graph Neural Network (HGNN) to extract features from molecular graphs of small-molecular drugs, thereby effectively capturing the hierarchical structural information within these graphs. To integrate heterogeneous information, a Multi-Manifold Feature Fusion Module combines structural features from the HGNN, chemical fingerprints, and semantic embeddings derived from pretrained language models. Extensive experiments on benchmark data sets demonstrate that our framework achieves superior performance compared with state-of-the-art Euclidean-based methods. The experimental results demonstrate that hyperbolic geometry offers significant advantages in extracting hierarchical features from non-Euclidean data and also highlight the promising potential of multimanifold feature fusion in the field of drug-target interaction prediction.
{"title":"MML-DTI: Multimanifold Learning with Hyperbolic Graph Neural Networks for Enhanced Drug-Target Interaction Prediction.","authors":"Haotian Guan,Tian Bai,Chuande Yang,Tao Zhang,Han Wang,Guishen Wang","doi":"10.1021/acs.jcim.5c02826","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02826","url":null,"abstract":"Accurately predicting drug-target interactions (DTIs) is crucial for drug discovery, repositioning. However, most deep learning-based DTI models are designed in Euclidean space, making it difficult to effectively represent the hierarchical and scale-free characteristics of biological data. Due to its unique negatively curved geometric properties, hyperbolic space can more effectively represent hierarchical relationships within data. Therefore, we propose a multimanifold learning framework that integrates multimodal features in hyperbolic and Euclidean spaces for drug-target interaction prediction. Specifically, we employ a Hyperbolic Graph Neural Network (HGNN) to extract features from molecular graphs of small-molecular drugs, thereby effectively capturing the hierarchical structural information within these graphs. To integrate heterogeneous information, a Multi-Manifold Feature Fusion Module combines structural features from the HGNN, chemical fingerprints, and semantic embeddings derived from pretrained language models. Extensive experiments on benchmark data sets demonstrate that our framework achieves superior performance compared with state-of-the-art Euclidean-based methods. The experimental results demonstrate that hyperbolic geometry offers significant advantages in extracting hierarchical features from non-Euclidean data and also highlight the promising potential of multimanifold feature fusion in the field of drug-target interaction prediction.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"37 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381341","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 : 2026-03-09DOI: 10.1021/acs.jcim.5c02840
Shivam Gupta,Taraknath Mandal
CC chemokine receptor type 5 (CCR5) functions as a key coreceptor facilitating HIV entry into host cells. Recent experimental findings suggest that CCR5 preferentially localizes at lipid domain boundaries within the host cell membrane, where its positioning enhances viral fusion efficiency by allowing the HIV fusion peptide gp41 to exploit the mechanically weaker interface regions. In this study, we employ coarse-grained molecular dynamics simulations to investigate the spatial organization of CCR5 within domain forming model membranes. Our results reveal a molecular mechanism by which CCR5 preferentially migrates and stabilizes at domain boundaries. Additionally, we show that lysophosphatidylcholine (lysoPC) lipids, acting as linactants, accumulate at domain interfaces, reduce line tension, and ultimately disrupt membrane domain organization. This disruption leads to a delocalization of CCR5, potentially impairing the ability of gp41 to target membrane boundaries for fusion. Together, our findings suggest that linactants may be employed to disrupt the spatial organization of CCR5, potentially hindering HIV's ability to initiate membrane fusion and entry.
{"title":"Controlling Spatial Organization of HIV Coreceptor CCR5.","authors":"Shivam Gupta,Taraknath Mandal","doi":"10.1021/acs.jcim.5c02840","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02840","url":null,"abstract":"CC chemokine receptor type 5 (CCR5) functions as a key coreceptor facilitating HIV entry into host cells. Recent experimental findings suggest that CCR5 preferentially localizes at lipid domain boundaries within the host cell membrane, where its positioning enhances viral fusion efficiency by allowing the HIV fusion peptide gp41 to exploit the mechanically weaker interface regions. In this study, we employ coarse-grained molecular dynamics simulations to investigate the spatial organization of CCR5 within domain forming model membranes. Our results reveal a molecular mechanism by which CCR5 preferentially migrates and stabilizes at domain boundaries. Additionally, we show that lysophosphatidylcholine (lysoPC) lipids, acting as linactants, accumulate at domain interfaces, reduce line tension, and ultimately disrupt membrane domain organization. This disruption leads to a delocalization of CCR5, potentially impairing the ability of gp41 to target membrane boundaries for fusion. Together, our findings suggest that linactants may be employed to disrupt the spatial organization of CCR5, potentially hindering HIV's ability to initiate membrane fusion and entry.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381342","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 : 2026-03-09DOI: 10.1021/acs.jcim.6c00060
Florence Szczepaniak,Donghyuk Suh,Wonpil Im
Recent advances in machine learning (ML) have enabled new developments in molecular dynamics simulation. Neural network potentials (NNPs) trained on quantum mechanical (QM) data provide highly accurate descriptions of drug-like molecules. Analogous to a QM and molecular mechanical (QM/MM) approach, hybrid ML/MM simulations employ NNPs to describe a localized region of the system, such as a ligand, while the rest of the system is treated using classical MM force fields. This hybrid framework enables simulations of protein-ligand complexes with near-QM accuracy for the ligand at a substantially reduced computational cost. CHARMM-GUI Hybrid ML/MM Builder automates the preparation of system and input files required for hybrid ML/MM modeling and simulation. This new module generates all necessary files to simulate protein-ligand complexes in solution or membrane using TorchANI-AMBER and OpenMM-ML. Currently supported NNPs include MACE and ANI. In this paper, we present Hybrid ML/MM Builder and representative application systems that demonstrate its usage and capabilities.
{"title":"CHARMM-GUI Hybrid ML/MM Builder for Hybrid Machine Learning and Molecular Mechanical Modeling and Simulations.","authors":"Florence Szczepaniak,Donghyuk Suh,Wonpil Im","doi":"10.1021/acs.jcim.6c00060","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c00060","url":null,"abstract":"Recent advances in machine learning (ML) have enabled new developments in molecular dynamics simulation. Neural network potentials (NNPs) trained on quantum mechanical (QM) data provide highly accurate descriptions of drug-like molecules. Analogous to a QM and molecular mechanical (QM/MM) approach, hybrid ML/MM simulations employ NNPs to describe a localized region of the system, such as a ligand, while the rest of the system is treated using classical MM force fields. This hybrid framework enables simulations of protein-ligand complexes with near-QM accuracy for the ligand at a substantially reduced computational cost. CHARMM-GUI Hybrid ML/MM Builder automates the preparation of system and input files required for hybrid ML/MM modeling and simulation. This new module generates all necessary files to simulate protein-ligand complexes in solution or membrane using TorchANI-AMBER and OpenMM-ML. Currently supported NNPs include MACE and ANI. In this paper, we present Hybrid ML/MM Builder and representative application systems that demonstrate its usage and capabilities.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"6 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381343","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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