AIMC Topic: Models, Chemical
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Automated design of multi-target ligands by generative deep learning.
Nature communications
Sep 11, 2024
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
Aug 11, 2024
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
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, ...
Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography.
Journal of chromatography. A
Jul 9, 2024
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
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...
Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose.
Environmental geochemistry and health
Jun 26, 2024
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...
The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell.
Environmental monitoring and assessment
Jun 19, 2024
Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo red (CR) dye from aqueous solutions, and a modelling study was conducted using artificial neural networks (ANNs). The structure and characteristic fun...
Predicting Cd(II) adsorption capacity of biochar materials using typical machine learning models for effective remediation of aquatic environments.
The Science of the total environment
Jun 13, 2024
The screening and design of "green" biochar materials with high adsorption capacity play a pivotal role in promoting the sustainable treatment of Cd(II)-containing wastewater. In this study, six typical machine learning (ML) models, namely Linear Reg...
A parameter estimation method for chromatographic separation process based on physics-informed neural network.
Journal of chromatography. A
Jun 12, 2024
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computat...
Data-driven, explainable machine learning model for predicting volatile organic compounds' standard vaporization enthalpy.
Chemosphere
May 6, 2024
The accurate prediction of standard vaporization enthalpy (ΔH°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental metho...
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