AIMC Topic: Solubility

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Virtual Screening for Reactive Natural Products and Their Probable Artifacts of Solvolysis and Oxidation.

Biomolecules Oct 27, 2020
Chemically unstable natural products are prone to show their reactivity in the procedures of extraction, purification, or identification and turn into contaminants as so-called "artifacts". However, identification of artifacts requires considerable i...
Drug Evaluation, Preclinical Solubility Biological Products Oxidation-Reduction Deep Learning
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Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures.

Physical chemistry chemical physics : PCCP Oct 16, 2020
Deep learning based methods have been widely applied to predict various kinds of molecular properties in the pharmaceutical industry with increasingly more success. In this study, we propose two novel models for aqueous solubility predictions, based ...
Water Solubility Pharmaceutical Preparations Datasets as Topic Databases, Chemical Deep Learning
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Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach.

Journal of chemical information and modeling Sep 1, 2020
Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming...
Deep Learning Humans Algorithms Biological Availability Solubility Caco-2 Cells
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Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction.

Molecular pharmaceutics Jun 19, 2020
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Model...
Computer Simulation Water Models, Molecular Software Algorithms Solubility Calorimetry, Differential Scanning Freezing Models, Chemical Pharmaceutical Preparations Machine Learning Thermodynamics Quantitative Structure-Activity Relationship Transition Temperature
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Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.

AAPS PharmSciTech Mar 31, 2020
Low solubility of active pharmaceutical compounds (APIs) remains an important challenge in dosage form development process. In the manuscript, empirical models were developed and analyzed in order to predict dissolution of bicalutamide (BCL) from sol...
Technology, Pharmaceutical Nitriles Tosyl Compounds Anilides Solubility Powders Artificial Intelligence Machine Learning
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Machine learning as a tool to design glasses with controlled dissolution for healthcare applications.

Acta biomaterialia Feb 28, 2020
The advancement of glass science has played a pivotal role in enhancing the quality and length of human life. However, with an ever-increasing demand for glasses in a variety of healthcare applications - especially with controlled degradation rates -...
Supervised Machine Learning Biocompatible Materials Glass Eyeglasses Solubility Oxides Equipment Design
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Analysis of phthalate plasticizer migration from PVDC packaging materials to food simulants using molecular dynamics simulations and artificial neural network.

Food chemistry Feb 22, 2020
Based on the experimental data of gas chromatography-mass spectrometry, an improved artificial neural network was first established to predict the migration of 2-ethylhexyl phthalate (DEHP) plasticizer from poly(vinylidene chloride) (PVDC) into food ...
Food Packaging Food Contamination Gas Chromatography-Mass Spectrometry Solubility Molecular Dynamics Simulation Neural Networks, Computer Temperature Diethylhexyl Phthalate Plasticizers Polyvinyl Chloride
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Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets.

Journal of chemical information and modeling Feb 3, 2020
Machine learning approaches have had tremendous success in various disciplines. However, such success highly depends on the size and quality of datasets. Scientific datasets are often small and difficult to collect. Currently, improving machine learn...
Machine Learning Deep Learning Computational Biology Solubility
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A deep learning approach for the blind logP prediction in SAMPL6 challenge.

Journal of computer-aided molecular design Jan 30, 2020
Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed u...
Water Molecular Structure Models, Chemical Drug Discovery Deep Learning Octanols Solubility Thermodynamics Machine Learning
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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Journal of medicinal chemistry Aug 27, 2019
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
Solubility Datasets as Topic Proof of Concept Study Databases, Chemical Organic Chemicals Models, Molecular Drug Discovery Deep Learning
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