AIMC Topic: Cheminformatics

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HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction.

Journal of chemical information and modeling Feb 3, 2025
In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research. Chemical reaction representations provide a link between NLP models ...
Natural Language Processing Cheminformatics Machine Learning
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Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.

Bioorganic chemistry Oct 28, 2024
The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literatu...
Artificial Intelligence Machine Learning Humans Ligands Cheminformatics Proteins Molecular Structure Drug Discovery
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Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

Molecular informatics Oct 15, 2024
This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characterist...
Quantitative Structure-Activity Relationship Cheminformatics Organic Chemicals Corrosion Machine Learning
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Deciphering Molecular Embeddings with Centered Kernel Alignment.

Journal of chemical information and modeling Sep 25, 2024
Analyzing machine learning models, especially nonlinear ones, poses significant challenges. In this context, centered kernel alignment (CKA) has emerged as a promising model analysis tool that assesses the similarity between two embeddings. CKA's eff...
Machine Learning Models, Molecular Algorithms Neural Networks, Computer Cheminformatics
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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 Humans Neural Networks, Computer Drug Discovery Cheminformatics
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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 ...
Cheminformatics Algorithms Programming Languages Software Machine Learning
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Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.

Critical reviews in toxicology Sep 3, 2024
This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and...
Structure-Activity Relationship Quantitative Structure-Activity Relationship Animals Cheminformatics Machine Learning Humans
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Cheminformatics analysis of indoleamine and tryptophan 2,3-dioxygenase inhibitors: A descriptor and fingerprint based machine learning approach to disclose selectivity measures.

Computers in biology and medicine Aug 1, 2024
Indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) are attractive drug targets for cancer immunotherapy. After disappointing results of the epacadostat as a selective IDO inhibitor in phase III clinical trials, there is much inter...
Tryptophan Oxygenase Cheminformatics Humans Enzyme Inhibitors Molecular Docking Simulation Indoleamine-Pyrrole 2,3,-Dioxygenase Machine Learning
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Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction.

Journal of chemical information and modeling Jul 15, 2024
Chemical information disseminated in scientific documents offers an untapped potential for deep learning-assisted insights and breakthroughs. Automated extraction efforts have shifted from resource-intensive manual extraction toward applying machine ...
Deep Learning Data Mining Cheminformatics
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OpenChemIE: An Information Extraction Toolkit for Chemistry Literature.

Journal of chemical information and modeling Jul 1, 2024
Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly inv...
Machine Learning Cheminformatics Data Mining Algorithms Databases, Chemical
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