AIMC Topic: Models, Chemical

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Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.

PloS one Jun 25, 2021
Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis a...
Machine Learning Monte Carlo Method Proteins Aptamers, Nucleotide Sequence Analysis, RNA Models, Chemical Computer Simulation Molecular Docking Simulation
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Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

PloS one May 27, 2021
Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resou...
Computer Simulation Groundwater Models, Chemical Water Quality Artificial Intelligence
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On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques.

BioMed research international Apr 26, 2021
Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications i...
Polystyrenes Glycolipids Models, Chemical Surface-Active Agents Machine Learning Adsorption
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Machine learning guided aptamer refinement and discovery.

Nature communications Apr 22, 2021
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, ...
Aptamers, Nucleotide Machine Learning Protein Binding Computer Simulation Ligands Models, Chemical Drug Discovery Gene Library Lipocalin-2
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Kinetic modeling and quasi-economic analysis of fermentative glycolipopeptide biosurfactant production in a medium co-optimized by statistical and neural network approaches.

Preparative biochemistry & biotechnology Apr 21, 2021
This study presents the kinetics of production of a glycolipopeptide biosurfactant in a medium previously co-optimized by response surface and neural network methods to gain some insight into its volumetric and specific productivities for possible sc...
Glycolipids Lipopeptides Fermentation Industrial Microbiology Surface-Active Agents Kinetics Neural Networks, Computer Models, Chemical
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Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks.

Journal of chemical information and modeling Feb 5, 2021
Solvation free energy is a fundamental property that influences various chemical and biological processes, such as reaction rates, protein folding, drug binding, and bioavailability of drugs. In this work, we present a deep learning method based on g...
Models, Chemical Solvents Neural Networks, Computer Thermodynamics Entropy
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Generative chemistry: drug discovery with deep learning generative models.

Journal of molecular modeling Feb 4, 2021
The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, image...
Drug Discovery Deep Learning Models, Chemical Neural Networks, Computer Artificial Intelligence
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Evaluating Deep Learning models for predicting ALK-5 inhibition.

PloS one Jan 28, 2021
Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the...
Drug Evaluation, Preclinical Protein Kinase Inhibitors Computational Biology Models, Chemical Receptor, Transforming Growth Factor-beta Type I Humans Deep Learning
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Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Chemical research in toxicology Dec 21, 2020
Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an screening in the early stage...
Chemical and Drug Induced Liver Injury Humans Deep Learning Models, Chemical Molecular Structure Pharmaceutical Preparations
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On the many-body nature of intramolecular forces in FFLUX and its implications.

Journal of computational chemistry Oct 27, 2020
FFLUX is a biomolecular force field under construction, based on Quantum Chemical Topology (QCT) and machine learning (kriging), with a minimalistic and physically motivated design. A detailed analysis of the forces within the kriging models as treat...
Models, Chemical Computer Simulation Water Machine Learning
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