AIMC Journal:
Journal of chemical information and modeling

Showing 231 to 240 of 934 articles

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Journal of chemical information and modeling Jul 16, 2024
Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to...
Machine Learning Algorithms Drug Discovery Quantum Theory Antimalarials Quantitative Structure-Activity Relationship
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Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction.

Journal of chemical information and modeling Jul 16, 2024
N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development and progression of many cancers. Accurate identification of m7G modification sites is essential for understandin...
Deep Learning RNA Nucleotide Motifs Guanosine
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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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3DReact: Geometric Deep Learning for Chemical Reactions.

Journal of chemical information and modeling Jul 15, 2024
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, ...
Neural Networks, Computer Deep Learning Models, Molecular Models, Chemical
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Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals.

Journal of chemical information and modeling Jul 15, 2024
We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic ...
Machine Learning Models, Molecular Electrons Crystallization
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Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions.

Journal of chemical information and modeling Jul 12, 2024
Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A no...
Machine Learning Software Computer-Aided Design Drug Design Stereoisomerism
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AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology.

Journal of chemical information and modeling Jul 9, 2024
Rapid and accurate calculation of acid dissociation constant (p) is crucial for designing chemical synthesis routes, optimizing catalysts, and predicting chemical behavior. Despite recent progress in machine learning, predicting solvation acidity, es...
Neural Networks, Computer Machine Learning Models, Chemical Hydrogen-Ion Concentration Solubility Solvents Acids
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Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds.

Journal of chemical information and modeling Jul 9, 2024
In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves ...
Deep Learning Magnetic Resonance Spectroscopy
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Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Journal of chemical information and modeling Jul 8, 2024
Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due t...
Drug Discovery Deep Learning Proteins Protein Domains Pharmaceutical Preparations Ligands
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Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

Journal of chemical information and modeling Jul 8, 2024
In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of co...
Humans Neural Networks, Computer Machine Learning Pharmaceutical Preparations Microsomes, Liver Quantitative Structure-Activity Relationship Stereoisomerism
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