AIMC Journal:
Journal of chemical information and modeling

Showing 201 to 210 of 934 articles

Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry.

Journal of chemical information and modeling Sep 20, 2024
A knowledge graph (KG) is a technique for modeling entities and their interrelations. Knowledge graph embedding (KGE) translates these entities and relationships into a continuous vector space to facilitate dense and efficient representations. In the...
Databases, Chemical Neural Networks, Computer Humans Cheminformatics Drug Discovery
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MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

Journal of chemical information and modeling Sep 17, 2024
The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the ...
Machine Learning Software Algorithms Programming Languages Cheminformatics
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MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network.

Journal of chemical information and modeling Sep 17, 2024
Current studies have demonstrated that microbe-host interactions (MHIs) play important roles in human public health. Therefore, identifying the interactions between microbes and hosts is beneficial to understanding the role of the microbiome and thei...
Humans Bacteria Host Microbial Interactions Computational Biology Deep Learning Host-Pathogen Interactions
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In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery.

Journal of chemical information and modeling Sep 17, 2024
TBK1, or TANK-binding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a crucial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and an...
Quantitative Structure-Activity Relationship Protein Kinase Inhibitors Machine Learning Protein Serine-Threonine Kinases Humans Computer Simulation Drug Discovery Models, Molecular
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Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.

Journal of chemical information and modeling Sep 17, 2024
In the quest for advanced superconducting materials, the accurate prediction of critical temperatures () poses a formidable challenge, largely due to the complex interdependencies between superconducting properties and the chemical and structural cha...
Machine Learning Temperature Superconductivity
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Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Journal of chemical information and modeling Sep 16, 2024
Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Compet...
Molecular Dynamics Simulation Ligands Protein Conformation Binding Sites Drug Discovery Protein Binding Machine Learning Proteins
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RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features.

Journal of chemical information and modeling Sep 14, 2024
The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental mea...
Computational Biology RNA Machine Learning Nucleic Acid Conformation
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A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

Journal of chemical information and modeling Sep 12, 2024
The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence...
Models, Molecular RNA Machine Learning Small Molecule Libraries Nucleic Acid Conformation
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Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach.

Journal of chemical information and modeling Sep 11, 2024
Unraveling the reaction pathway of photoinduced reactions poses a great challenge owing to its complexity. Recently, graph theory-based machine learning combined with nonadiabatic molecular dynamics (NAMD) has been applied to obtain the global reacti...
Retinaldehyde Molecular Dynamics Simulation Photochemical Processes Machine Learning Quantum Theory Isomerism Azo Compounds
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Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Journal of chemical information and modeling Sep 10, 2024
Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among publish...
Models, Molecular Organic Cation Transport Proteins Neural Networks, Computer Machine Learning Humans
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