Journal of Chemical Information and Modeling 最新文献_第2页

Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum. 通过最大化包含最优的概率的批处理贝叶斯优化。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-05 DOI: 10.1021/acs.jcim.5c00214

Jenna Fromer,Runzhong Wang,Mrunali Manjrekar,Austin Tripp,José Miguel Hernández-Lobato,Connor W Coley

{"title":"Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum.","authors":"Jenna Fromer,Runzhong Wang,Mrunali Manjrekar,Austin Tripp,José Miguel Hernández-Lobato,Connor W Coley","doi":"10.1021/acs.jcim.5c00214","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00214","url":null,"abstract":"Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation. This often involves optimizing nonadditive batch acquisition functions, necessitating approximation via myopic construction and/or diversity heuristics. In this work, we propose an acquisition strategy for discrete optimization that is motivated by pure exploitation, qPO (multipoint Probability of Optimality). qPO maximizes the probability that the batch includes the true optimum, which is expressed as the sum over individual acquisition scores and thereby circumvents the combinatorial challenge of optimizing a batch acquisition function. We differentiate the proposed strategy from parallel Thompson sampling and discuss how it implicitly captures diversity. Finally, we apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"115 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914915","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}

引用次数: 0

Alternative Algebraic Perspectives on CO/H2 PROX over MnO2 Composite Catalysts. CO/H2 PROX在MnO2复合催化剂上的替代代数观点。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-02 DOI: 10.1021/acs.jcim.5c00072

Marco Bertini,Francesco Ferrante,Laura Gueci,Antonio Prestianni,Dario Duca,Francesco Arena,Dmitry Yu Murzin

{"title":"Alternative Algebraic Perspectives on CO/H2 PROX over MnO2 Composite Catalysts.","authors":"Marco Bertini,Francesco Ferrante,Laura Gueci,Antonio Prestianni,Dario Duca,Francesco Arena,Dmitry Yu Murzin","doi":"10.1021/acs.jcim.5c00072","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00072","url":null,"abstract":"This study presents a graph-based approach to investigate the steady-state kinetics of the preferential CO oxidation process in H2 (PROX) occurring on a MnO2 model fragment with manganese centers at varying oxidation states, simulating the surface Mn(IV) active sites of a composite MnO2-CeO2 catalyst previously used in experimental applications. A novel modeling approach, termed DFT graph-based kinetic analysis (DFT-GKA), is introduced. It utilizes free activation energy (ΔG⧧) values to characterize linear elementary events, supposed at pseudosteady-state, in this complex reaction system, as determined through density functional theory (DFT) integrated by thermochemical calculations. The implementation of this model is achieved using a homemade Common Lisp code, specifically designed for efficient manipulation of long lists essential for the analysis. Finally, the comprehensive ab initio DFT kinetic descriptors related to the CO/H2 PROX catalytic process on the manganese oxide fragments are discussed, highlighting their significance for future research and applications.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"137 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902894","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}

引用次数: 0

Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from Burkholderia spp. 伯克氏菌中Pen β-内酰胺酶的进化动力学和功能差异

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-02 DOI: 10.1021/acs.jcim.5c00271

Jing Gu,Pratul K Agarwal,Robert A Bonomo,Shozeb Haider

{"title":"Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from Burkholderia spp.","authors":"Jing Gu,Pratul K Agarwal,Robert A Bonomo,Shozeb Haider","doi":"10.1021/acs.jcim.5c00271","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00271","url":null,"abstract":"Antimicrobial resistance (AMR) is a global threat, with Burkholderia species contributing significantly to difficult-to-treat infections. The Pen family of β-lactamases are produced by all Burkholderia spp., and their mutation or overproduction leads to the resistance of β-lactam antibiotics. Here we investigate the dynamic differences among four Pen β-lactamases (PenA, PenI, PenL and PenP) using machine learning driven enhanced sampling molecular dynamics simulations, Markov State Models (MSMs), convolutional variational autoencoder-based deep learning (CVAE) and the BindSiteS-CNN model. In spite of sharing the same catalytic mechanisms, these enzymes exhibit distinct dynamic features due to low sequence identity, resulting in different substrate profiles and catalytic turnover. The BindSiteS-CNN model further reveals local active site dynamics, offering insights into the Pen β-lactamase evolutionary adaptation. Our findings reported here identify critical mutations and propose new hot spots affecting Pen β-lactamase flexibility and function, which can be used to fight emerging resistance in these enzymes.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"54 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902898","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}

引用次数: 0

Machine Learning Classification of Chirality and Optical Rotation Using a Simple One-Hot Encoded Cartesian Coordinate Molecular Representation. 手性和旋光性的机器学习分类，使用简单的单热编码笛卡尔坐标分子表示。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-01 DOI: 10.1021/acs.jcim.4c02374

Yilin Zhou,Haoran Zhu,Yijie Yuan,Ziyu Song,Brendan C Mort

引用次数: 0

SF-Rx: A Multioutput Deep Neural Network-Based Framework Predicting Drug-Drug Interaction under Realistic Conditions for Safe Prescription. SF-Rx：基于多输出深度神经网络的药物相互作用预测框架。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-01 DOI: 10.1021/acs.jcim.5c00075

Daeun Kim,Jaehong Yu,Sang-Hun Bae,Jihyun Lee

引用次数: 0

Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design. Hsp90 n端结构域肽抑制剂的双位点靶向：机制和设计。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-01 DOI: 10.1021/acs.jcim.5c00629

Min Zang,Haipeng Gan,Xuejie Zhou,Lei Wang,Hao Dong

{"title":"Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design.","authors":"Min Zang,Haipeng Gan,Xuejie Zhou,Lei Wang,Hao Dong","doi":"10.1021/acs.jcim.5c00629","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00629","url":null,"abstract":"Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target for cancer therapy. However, the design of effective Hsp90 inhibitors presents substantial challenges due to the complex interaction network and the requisite specificity of the inhibitors. This study tackles the task of designing peptide inhibitors capable of concurrently binding to both the ATP-binding pocket and the Cdc37-binding site within the N-terminal domain of Hsp90. In response to these challenges, we developed an advanced peptide screening protocol that merges machine learning with various molecular simulation techniques to boost the identification and optimization of potent inhibitors. Our integrated approach employs a convolutional neural network-based framework to predict peptide binding propensities. This predictive model is augmented by comprehensive molecular docking and dynamic simulations to assess the stability and interaction dynamics of Hsp90/peptide complexes. We successfully identified three heptapeptides that demonstrate the ability to interact with both binding sites, effectively obstructing the entrance to the ATP-binding pocket. This study elucidates the inhibitory mechanisms of these peptides, paves the way for the development of more efficacious therapeutic agents targeting Hsp90, and underscores the value of integrating machine learning techniques with molecular modeling in the peptide design process.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"42 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902975","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}

引用次数: 0

EVOLVE: A Web Platform for AI-Based Protein Mutation Prediction and Evolutionary Phase Exploration. 进化：一个基于人工智能的蛋白质突变预测和进化阶段探索的Web平台。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-01 DOI: 10.1021/acs.jcim.5c00026

Satyam Sangeet,Anushree Sinha,Madhav B Nair,Arpita Mahata,Raju Sarkar,Susmita Roy

{"title":"EVOLVE: A Web Platform for AI-Based Protein Mutation Prediction and Evolutionary Phase Exploration.","authors":"Satyam Sangeet,Anushree Sinha,Madhav B Nair,Arpita Mahata,Raju Sarkar,Susmita Roy","doi":"10.1021/acs.jcim.5c00026","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00026","url":null,"abstract":"While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effects on protein structure and function. Addressing the challenges of large sequence-space analysis, we present EVOLVE, a web tool enabling researchers to explore prospective mutation sites and their collective behavior. EVOLVE integrates a statistical mechanics-guided machine learning algorithms to predict probable mutational sites, with statistical mechanics calculating mutational entropy to accurately identify mutational hotspots. Validation against a number of viral protein sequences confirms its ability to predict mutations and their functional consequences. By leveraging statistical mechanics of phase transition concept, EVOLVE also quantifies mutational entropy fluctuations, offering a quantitative foundation for identifying Variants of Concern (VOC) or Variants under Monitoring (VUM) as per World Health Organization (WHO) guidelines. EVOLVE streamlines data upload and analysis with a user-friendly interface and comprehensive tutorials. Access EVOLVE free at https://evolve-iiserkol.com.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"11 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897420","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}

引用次数: 0

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation. 基于小样本学习和LLM精馏的化学命名实体快速自适应识别。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-05-01 DOI: 10.1021/acs.jcim.5c00248

Yue Zhang,Dionisios G Vlachos,Dongxia Liu,Hui Fang

{"title":"Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation.","authors":"Yue Zhang,Dionisios G Vlachos,Dongxia Liu,Hui Fang","doi":"10.1021/acs.jcim.5c00248","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00248","url":null,"abstract":"Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"39 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902977","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}

引用次数: 0

Computational Exploration of the Inhibitory Mechanism of mRNA against the Phase Separation of hnRNPA2 Low Complexity Domains. mRNA对hnRNPA2低复杂度结构域相分离抑制机制的计算探索。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-04-30 DOI: 10.1021/acs.jcim.5c00321

Yuan Tan,Yujie Chen,Tong Pan,Yiming Tang,Xianshi Liu,Yawei Yu,Guanghong Wei

{"title":"Computational Exploration of the Inhibitory Mechanism of mRNA against the Phase Separation of hnRNPA2 Low Complexity Domains.","authors":"Yuan Tan,Yujie Chen,Tong Pan,Yiming Tang,Xianshi Liu,Yawei Yu,Guanghong Wei","doi":"10.1021/acs.jcim.5c00321","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00321","url":null,"abstract":"hnRNPA2, an RNA-binding protein involved in RNA metabolism and regulation, can undergo liquid-liquid phase separation (LLPS) to form dynamic biomolecular condensates. Previous experiments have reported that RNA molecules can inhibit the LLPS of the hnRNPA2 low complexity domain (LCD). However, the atomistic mechanisms underlying this inhibitory effect and RNA-LCD interactions remain largely elusive. Herein, the influence of mRNA A2RE11 on the single-chain conformational ensemble and transient interactions between LCD chains are investigated through all-atom-enhanced sampling molecular dynamics (MD) simulations. Our simulations reveal that aromatic residues are essential to intrachain interactions of single-chain hnRNPA2 LCDs as well as interchain interactions of LCD dimers. Through binding to aromatic and positively charged residues of the hnRNPA2 LCD, A2RE11 undermines the degree of collapse of the single-chain LCD and disrupts the aromatic stacking, hydrogen bonding, and cation-π interchain interactions. Our coarse-grained phase coexistence MD simulations further underscore the preeminence of interchain aromatic and cation-π interactions in regulating the phase behavior of hnRNPA2 LCD and the RNA binding affinity for the RGG and Y/FG(G) motifs. These findings from multiscale simulations lead to a greater appreciation of the complex interaction network underlying the phase separation and RNA-protein interaction of the hnRNPA2 LCD.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897452","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}

引用次数: 0

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery. MHNfs：提示低数据药物发现的上下文生物活性预测。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-04-30 DOI: 10.1021/acs.jcim.4c02373

Johannes Schimunek,Sohvi Luukkonen,Günter Klambauer

引用次数: 0