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Journal of Chemical Information and Modeling

The Journal of Chemical Information and Modeling is a peer-reviewed scientific journal that focuses on the dissemination of research related to the development and application of computational methods in chemistry. It covers topics such as molecular modeling, drug design, chemoinformatics, and data analysis. The journal serves as a platform for researchers to share their findings and advancements in the field of chemical information and modeling.
Details
Abbr. J Chem Inf Model
Publisher American Chemical Society
Start 2005
End Continuing
Frequency Monthly, 2009-
p-ISSN 1549-9596
e-ISSN 1549-960X
Country United States
Language English
Specialties Chemistry
Medical Informatics
Metrics
h-index / Ranks: 789 192
SJR / Ranks: 2199 1396
CiteScore / Ranks: 1502 9.80
JIF / Ranks: 1276 5.6
Recent Articles
1.
BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and
Cai J, Yan J, Un C, Wang Y, Campbell-Valois F, Siu S
J Chem Inf Model . 2025 Mar; PMID: 40086449
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...
2.
Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al-Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation
Chang Z, Feng L, Xue H, Yang Y, Ren J, Tang F, et al.
J Chem Inf Model . 2025 Mar; PMID: 40085549
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...
3.
Employing Automated Machine Learning (AutoML) Methods to Facilitate the ADMET Properties Prediction
Han H, Shaker B, Lee J, Choi S, Yoon S, Singh M, et al.
J Chem Inf Model . 2025 Mar; PMID: 40085003
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...
4.
Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation
Zhou J, Li C, Yue Y, Kim Y, Park S
J Chem Inf Model . 2025 Mar; PMID: 40084909
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...
5.
Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction
Wang Y, Liu X, Zhang Y, Wang X, Xia K
J Chem Inf Model . 2025 Mar; PMID: 40082211
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...
6.
FDPSM: Feature-Driven Prediction Modeling of Pathogenic Synonymous Mutations
Jin F, Cheng N, Wang L, Ye B, Xia J
J Chem Inf Model . 2025 Mar; PMID: 40082068
Synonymous mutations, once considered to be biologically neutral, are now recognized to affect protein expression and function by altering the RNA splicing, stability, or translation efficiency. These effects can contribute...
7.
UMPPI: Unveiling Multilevel Protein-Peptide Interaction Prediction via Language Models
Xiong S, Cai J, Shi H, Cui F, Zhang Z, Wei L
J Chem Inf Model . 2025 Mar; PMID: 40077987
Protein-peptide interactions are essential to cellular processes and disease mechanisms. Identifying protein-peptide binding residues is critical for understanding peptide function and advancing drug discovery. However, experimental methods are costly and...
8.
Fluor-Predictor: An Interpretable Tool for Multiproperty Prediction and Retrieval of Fluorescent Dyes
Song W, Xiong L, Li X, Zhang Y, Wang B, Liu G, et al.
J Chem Inf Model . 2025 Mar; PMID: 40071775
With the rapid advancements in the field of fluorescent dyes, accurate prediction of optical properties and efficient retrieval of dye-related data are essential for effective dye design. However, there is...
9.
Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning
Gan Y, Li S, Xu G, Yan C, Zou G
J Chem Inf Model . 2025 Mar; PMID: 40071716
The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently...
10.
Cocry-pred: A Dynamic Resource Propagation Method for Cocrystal Prediction
Song W, Peng R, Yu H, Zhan M, Liu G, Li W, et al.
J Chem Inf Model . 2025 Mar; PMID: 40070082
Drug cocrystallization is a powerful strategy to enhance drug properties by modifying their physicochemical characteristics without altering their chemical structure. However, the identification of suitable coformers remains a challenging and...