AIMC Topic: Databases, Chemical

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The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge.

SLAS discovery : advancing life sciences R & D Feb 3, 2024
The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to predict the aqueous solubility of small molecules using experimental data from 100 K compounds. In total, hundred teams took part in the challenge to predict low, m...
Consensus Solubility Databases, Chemical Neural Networks, Computer Algorithms
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Data-Driven Elucidation of Flavor Chemistry.

Journal of agricultural and food chemistry Apr 27, 2023
Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challen...
Machine Learning Databases, Factual Computer Simulation Humans Artificial Intelligence Databases, Chemical
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Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning.

Biomolecules Mar 27, 2022
The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods use...
Deep Learning Databases, Chemical Drug Design
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Quantitative structure retention relationship (QSRR) modelling for Analytes' retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Jan 19, 2022
In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived re...
Machine Learning Algorithms Organic Chemicals Linear Models Databases, Chemical Chromatography, Liquid Molecular Structure Mass Spectrometry Bayes Theorem Metabolomics
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DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

Journal of chemical information and modeling Dec 27, 2021
A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting crit...
Neural Networks, Computer Databases, Factual Cluster Analysis Databases, Chemical Reproducibility of Results
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Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning.

International journal of molecular sciences Oct 26, 2021
The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different ...
Databases, Pharmaceutical Glioblastoma Databases, Chemical Antineoplastic Agents User-Computer Interface Drug Carriers Drug Screening Assays, Antitumor Nanoparticles Drug Delivery Systems Brain Neoplasms Drug Design Humans Machine Learning
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Stacked Ensemble Machine Learning for Range-Separation Parameters.

The journal of physical chemistry letters Sep 24, 2021
Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the none...
Machine Learning Fluorescent Dyes Databases, Chemical Density Functional Theory
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Bandgap prediction of two-dimensional materials using machine learning.

PloS one Aug 13, 2021
The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect ba...
Databases, Chemical Molybdenum Machine Learning Disulfides Electric Conductivity Boron Compounds Semiconductors Models, Statistical Graphite
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In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Molecular diversity Jul 1, 2021
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focu...
Cheminformatics Drug-Related Side Effects and Adverse Reactions Machine Learning Quantitative Structure-Activity Relationship Databases, Chemical Algorithms ROC Curve Blood Cells Models, Molecular Humans Drug Discovery Reproducibility of Results Deep Learning
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Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

Molecular diversity Jun 23, 2021
DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discov...
Diacylglycerol O-Acyltransferase Cheminformatics Reproducibility of Results Databases, Chemical Humans Molecular Docking Simulation Molecular Structure Machine Learning ROC Curve Quantitative Structure-Activity Relationship Molecular Dynamics Simulation Algorithms Models, Molecular Enzyme Inhibitors Drug Discovery
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