AIMC Topic: Models, Chemical

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Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.

Environmental monitoring and assessment
Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hy...

Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning.

Environmental research
Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent...

Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes.

IEEE/ACM transactions on computational biology and bioinformatics
A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates t...

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Environmental science. Processes & impacts
Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attrib...

Automated design of multi-target ligands by generative deep learning.

Nature communications
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical...

A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches.

International journal of pharmaceutics
The pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO (scCO)-based techniques. Measuring the solubility of drugs in scCO at varying conditions is a crucial parameter ...

3DReact: Geometric Deep Learning for Chemical Reactions.

Journal of chemical information and modeling
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, ...

Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography.

Journal of chromatography. A
Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic...

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology.

Journal of chemical information and modeling
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...

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose.

Environmental geochemistry and health
This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characteriza...