Journal of Chemical Information and Modeling 最新文献

{"title":"<i>PolyConstruct</i>: Adapting Biomolecular Simulation Pipelines for Polymers with <i>PolyBuild</i>, <i>PolyConf</i>, and <i>PolyTop</i>.","authors":"Rangika Munaweera, Ada Quinn, Luna Morrow, Richard A Morris, Megan L O'Mara","doi":"10.1021/acs.jcim.4c02375","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02375","url":null,"abstract":"<p><p>Molecular dynamics simulations are invaluable tools that provide both a molecular understanding and a means for the rational design of polymers. A key bottleneck in current polymer molecular dynamics simulations is the lack of a comprehensive and generalizable method that streamlines the preparation of simulations for novel polymer architectures and chemistries. Here, we present <i>PolyConstruct</i>, a generalizable computational framework that leverages the GROMACS biomolecular simulation package for force field agnostic atomistic simulations of biocompatible and stimuli-responsive polymers. <i>PolyConstruct</i> contains three workflows, <i>PolyBuild</i>, <i>PolyTop</i>, and <i>PolyConf</i>, for generating chemically accurate topology parameters from monomer parameters and structural coordinates for complex polymer architectures and chemistries. We highlight the utility and robustness of <i>PolyBuild</i>, <i>PolyTop</i>, and <i>PolyConf</i> with examples of linear, branched, star, and dendritic polymers.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646374","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":"Accurate Prediction of CRISPR/Cas13a Guide Activity Using Feature Selection and Deep Learning.","authors":"Jiashun Fu, Xuyang Liu, Ruijie Deng, Xiue Jiang, Wensheng Cai, Haohao Fu, Xueguang Shao","doi":"10.1021/acs.jcim.4c02438","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02438","url":null,"abstract":"<p><p>CRISPR/Cas13a serves as a key tool for nucleic acid tests; therefore, accurate prediction of its activity is essential for creating robust and sensitive diagnosis. In this study, we create a dual-branch neural network model that achieves high prediction accuracy and classification performance across two independent CRISPR/Cas13a data sets, outperforming previously published models relying solely on sequence features. The model integrates direct sequence encoding with descriptive features and yields 99 key descriptive features out of 1553, extracted through statistical analysis, which critically influence guide-target interactions and Cas13a guide activity. By employing Shapley Additive Explanations and Integrated Gradients for feature importance analysis, we show that sequence composition, mismatch type and frequency, and the protospacer flanking site region are primary features. These findings underscore the importance of using descriptive features as complementary inputs to deep learning-based encoding and provide valuable insights into the mechanisms underlying guide-target interaction. All in all, this study not only introduces a reliable and efficient model for Cas13a guide activity prediction but also offers a foundation for future rational design efforts.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646377","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":"Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.","authors":"Jian Jiang, Long Chen, Yueying Zhu, Yazhou Shi, Huahai Qiu, Bengong Zhang, Tianshou Zhou, Guo-Wei Wei","doi":"10.1021/acs.jcim.5c00114","DOIUrl":"10.1021/acs.jcim.5c00114","url":null,"abstract":"<p><p>Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory receptors in the central nervous system, could enhance their inhibitory action, potentially reducing side effects while improving the potency of anesthetics. In this study, we introduce a proteomic learning of GABA receptor-mediated anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins in protein-protein interaction (PPI) networks and over 1.5 millions known binding compounds. We develop a corresponding drug-target interaction network to identify potential lead compounds for novel anesthetic design. To ensure robust proteomic learning predictions, we curated a data set comprising 136 targets from a pool of 980 targets within the PPI networks. We employed three machine learning algorithms, integrating advanced natural language processing (NLP) models such as pretrained transformers and autoencoder embeddings. Through a comprehensive screening process, we evaluated the side effects and repurposing potential of over 180,000 drug candidates targeting the GABRA5 receptor. Additionally, we assessed the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify those with near-optimal characteristics. This approach also involved optimizing the structures of existing anesthetics. Our work presents an innovative strategy for the development of new anesthetic drugs, optimization of anesthetic use, and a deeper understanding of potential anesthesia-related side effects.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646397","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":"Influence of Coverage Dependence on the Thermophysical Properties of Adsorbates and Its Impact on Microkinetic Models.","authors":"Jongyoon Bae, Bjarne Kreitz, Andrew A Peterson, C Franklin Goldsmith","doi":"10.1021/acs.jcim.4c02167","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02167","url":null,"abstract":"<p><p>This work focuses on the impact of lateral interactions on the thermophysical properties of adsorbates. We present different parametrizations for coverage-dependent enthalpy, entropy, and heat capacity in a mean-field microkinetic model. These models are tested against two systems, CO/Pt(111) and CO/Co(0001), using two different functionals. A detailed investigation into how coverage influences the thermophysical properties of CO* is presented. We place particular emphasis on studying the impact of coverage on the vibrational partition function and how this affects the entropy of adsorbates. Higher coverages typically lead to increased repulsive interactions, which should further constrain the large amplitude modes that contribute the most to the vibrational entropy. In some cases, however, the opposite effect occurred; the vibrational entropy actually increased because surface crowding forced adsorbates to different binding locations that had lower frequencies. Our results highlighted cases where coverage-dependent entropy should be included, such as for adsorbates with lateral vibrational modes and systems at high temperatures. These methods for including coverage-dependent properties into mean-field microkinetics in a thermodynamically consistent way are now available in the open-source software Cantera.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646392","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":"SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction.","authors":"Marltan Wilson, Thomas Coudrat, Andrew Warden","doi":"10.1021/acs.jcim.4c02230","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02230","url":null,"abstract":"<p><p>Accurately predicting protein-ligand interactions and enzymatic kinetics remains a challenge for computational biology. Here, we present SELFprot, a suite of modular transformer-based machine learning architectures that leverage the ESM2-35M model architecture for protein sequence and small molecule embeddings to improve predictions of complex biochemical interactions. SELFprot employs multitask learning and parameter-efficient finetuning through low-rank adaptation, allowing for adaptive, data-driven model refinement. Furthermore, ensemble learning techniques are used to enhance the robustness and reduce the prediction variance. Evaluated on the BindingDB and CatPred-DB data sets, SELFprot achieves competitive performance with notable improvements in parameter-efficient prediction of <b>k</b>_<b>cat</b>, <b>K</b>_<b>m</b>, <b>K</b>_<b>i</b>, <b>K</b>_<b>d</b>, <b>IC</b>_<b>50</b>, and <b>EC</b>_<b>50</b> values as well as the classification of functional site residues. With comparable accuracy to existing models and an order of magnitude fewer parameters, SELFprot demonstrates versatility and efficiency, making it a valuable tool for protein-ligand interaction studies in bioengineering.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646419","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":"Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation.","authors":"Junyu Zhou, Chen Li, Yu Yue, Yong Kwan Kim, Sunmin Park","doi":"10.1021/acs.jcim.5c00135","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00135","url":null,"abstract":"<p><p>Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with experimental validation to identify natural compounds with comprehensive neuroprotective properties. Our computational framework integrated SELFormer, a transformer-based deep learning model, and multiple deep learning algorithms to predict NC bioactivity against seven crucial stroke-related targets (<i>ACE, GLA, MMP9, NPFFR2, PDE4D</i>, and <i>eNOS</i>). The pipeline encompassed IC50 predictions, clustering analysis, quantitative structure-activity relationship (QSAR) modeling, and uniform manifold approximation and projection (UMAP)-based bioactivity profiling followed by molecular docking studies and experimental validation. Analysis revealed six distinct NC clusters with unique molecular signatures. UMAP projection identified 11 medium-activity (6 < pIC50 ≤ 7) and 57 high-activity (pIC50 > 7) compounds, with molecular docking confirming strong correlations between binding energies and predicted pIC50 values. <i>In vitro</i> studies using NGF-differentiated PC12 cells under oxygen-glucose deprivation demonstrated significant neuroprotective effects of four high-activity compounds: feruloyl glucose, l-hydroxy-l-tryptophan, mulberrin, and ellagic acid. These compounds enhanced cell viability, reduced acetylcholinesterase activity and lipid peroxidation, suppressed <i>TNF-</i>α expression, and upregulated <i>BDNF</i> mRNA levels. Notably, mulberrin and ellagic acid showed superior efficacy in modulating oxidative stress, inflammation, and neurotrophic signaling. This study establishes a robust deep learning-driven framework for identifying multitarget natural therapeutics for ischemic stroke. The validated compounds, particularly mulberrin and ellagic acid, are promising for stroke treatment development. Our findings demonstrate the effectiveness of integrating computational prediction with experimental validation in accelerating drug discovery for complex neurological disorders.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622866","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":"Employing Automated Machine Learning (AutoML) Methods to Facilitate the <i>In Silico</i> ADMET Properties Prediction.","authors":"Herim Han, Bilal Shaker, Jin Hee Lee, Sunghwan Choi, Sanghee Yoon, Maninder Singh, Shaherin Basith, Minghua Cui, Sunil Ahn, Junyoung An, Soosung Kang, Min Sun Yeom, Sun Choi","doi":"10.1021/acs.jcim.4c02122","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02122","url":null,"abstract":"<p><p>The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in <i>in silico</i> ADMET modeling has gained momentum due to its high-throughput and low-cost attributes. In this study, we developed a machine learning model capable of predicting 11 ADMET properties of chemical compounds. Each model was constructed by combining one of 40 classification algorithms including random forest (RF), extreme gradient boosting (XGB), support vector machine (SVM), and gradient boosting (GB) with one of three predefined hyperparameter configurations. This process can be efficiently performed using automated machine learning (AutoML) methods, which automatically search for the best combination of model algorithms and optimized hyperparameters. We developed optimal predictive models for 11 different ADMET properties using the Hyperopt-sklearn AutoML method. All of the developed models depicted an area under the ROC curve (AUC) >0.8. Furthermore, our developed models outperformed most of the ADMET properties and showed comparable performance in other properties when evaluated on external data sets and compared with published predictive models. Our results support the applicability of AutoML in ADMET prediction and will be helpful for ADMET prediction in early-stage drug discovery.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622859","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":"BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>.","authors":"Jianxiu Cai, Jielu Yan, Chonwai Un, Yapeng Wang, François-Xavier Campbell-Valois, Shirley W I Siu","doi":"10.1021/acs.jcim.4c01749","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01749","url":null,"abstract":"<p><p>Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately quantify AMP activity against specific bacteria, making the identification of highly potent AMPs a challenge. Here, we present a deep learning method, BERT-AmPEP60, based on the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) architecture to extract embedding features from input sequences. Using the transfer learning strategy, we built regression models to predict the minimum inhibitory concentration (MIC) of peptides for <i>Escherichia coli</i> (EC) and <i>Staphylococcus aureus</i> (SA). In five independent experiments with 10% leave-out sequences as the test sets, the optimal EC and SA models outperformed the state-of-the-art regression method and traditional machine learning methods, achieving an average mean squared error of 0.2664 and 0.3032 (log μM), respectively. They also showed a Pearson correlation coefficient of 0.7955 and 0.7530, and a Kendall correlation coefficient of 0.5797 and 0.5222, respectively. Our models outperformed existing deep learning and machine learning methods that rely on conventional sequence features. This work underscores the effectiveness of utilizing BERT with transfer learning for training quantitative AMP prediction models specific for different bacterial species. The web server of BERT-AmPEP60 can be found at https://app.cbbio.online/ampep/home. To facilitate development, the program source codes are available at https://github.com/janecai0714/AMP_regression_EC_SA.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629979","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":"Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al-Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation.","authors":"Zhen Chang, Li Feng, Hong-Tao Xue, Yan-Hong Yang, Jun-Qiang Ren, Fu-Ling Tang, Xue-Feng Lu, Jun-Chen Li","doi":"10.1021/acs.jcim.5c00008","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00008","url":null,"abstract":"<p><p>Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomic potentials. In this study, the deep potential (DP) approach, aided by high-throughput first-principles calculations, was utilized to develop an Al-Er interatomic potential specifically for MD simulations. Systematic comparisons between the physical properties (e.g., energy-volume curves, melting point, elastic constants) predicted by the DP model and those obtained from density functional theory (DFT) demonstrated that the developed DP model for Al-Er alloys possesses reliable predictive capabilities while retaining DFT-level accuracy. Our findings confirm that Al₃Er, Al₂Er, and AlEr₂ exhibit mechanical stability. The calculated melting point of Al₃Er (1398 K) shows a 57 K deviation from the experimental value (1341 K). With the Er content increasing from 0.01% to 0.064 at.% in Al-Er alloys, the grain boundary (GB) concentration of Er atoms increases from 0.03 to 0.07% following Monte Carlo (MC) annealing optimization. The Al-0.05 at.%Er alloy exhibits the highest yield strength, with an increase of 0.128 GPa (6.1%) compared to pure Al. For Al-0.05 at.%Er alloys with varying average grain sizes, the GB concentration of Er atoms increases by about 1.4-1.6 times after MC annealing compared to the average Er content. Additionally, the Al-Er alloys reach the peak yield strength of 2.214 GPa when the average grain size is 11.72 nm. The GB segregation of Er atoms lowers the system energy and thus enhances stability. Notable changes in the segregation behavior of Er atoms were observed with increasing Er concentration and decreasing grain size. These results would facilitate the understanding of the mechanical characteristics of Al-Er alloys and offer a theoretical basis for developing advanced nanopolycrystalline Al-Er alloys.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629980","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":"Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein–Ligand Binding Affinity Prediction","authors":"Yaxing Wang,&nbsp;Xiang Liu,&nbsp;Yipeng Zhang,&nbsp;Xiangjun Wang and Kelin Xia*,&nbsp;","doi":"10.1021/acs.jcim.4c0230910.1021/acs.jcim.4c02309","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02309https://doi.org/10.1021/acs.jcim.4c02309","url":null,"abstract":"<p >With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has been shown by topological data analysis (TDA) and TDA-based machine learning models in various steps of drug design and discovery. Here, we propose, for the first time, join persistent homology (JPH) and JPH-based machine learning models for metalloprotein–ligand binding affinity prediction. Mathematically, dramatically different from persistent homology and extended persistent homology, our JPH employs a set of filtration functions to generate a multistage filtration for the join of the original simplicial complex and a specially designed test simplicial complex. From the featurization perspective, our JPH-based molecular descriptors can provide a more comprehensive characterization of the intrinsic topological information of the data. Our JPH descriptors are combined with the gradient boosting tree (GBT) model for metalloprotein–ligand binding affinity prediction. The benchmark dataset for metalloprotein–ligand complexes from PDBbind-v2020 is employed for the validation and comparison of our model. It has been found that our JPH-GBT model can outperform all of the existing models, as far as we know. This demonstrates the great potential of our join persistent homology in the characterization of molecular structures and functions.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"2785–2793 2785–2793"},"PeriodicalIF":5.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675694","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}