AIMC Topic: Solubility

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Accurate Physical Property Predictions via Deep Learning.

Molecules (Basel, Switzerland)
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory ac...

Enzymatic extraction and functional properties of phosphatidylcholine from chicken liver.

Poultry science
An environmentally sustainable method to extract phosphatidylcholine (PC) from chicken liver (PCCL) and its functional properties were studied. The extraction times, enzymatic hydrolysis time, the solid-liquid ratio as well as types of enzymes (prota...

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

International journal of molecular sciences
Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharma...

Yield prediction of "Thermal-dissolution based carbon enrichment" treatment on biomass wastes through coupled model of artificial neural network and AdaBoost.

Bioresource technology
The "Thermal-dissolution based carbon enrichment" was proven as an efficient and homogenizing treatment method in converting biomass wastes into similar high-quality carbon materials. However, their yields varied significantly with respect to the dif...

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.

Journal of computer-aided molecular design
Accurate prediction of lipophilicity-logP-based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps...

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Interdisciplinary sciences, computational life sciences
BACKGROUND: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequ...

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning.

International journal of molecular sciences
Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and tern...

Characterizing the Impact of Spray Dried Particle Morphology on Tablet Dissolution Using Quantitative X-Ray Microscopy.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
For oral solid dosage forms, disintegration and dissolution properties are closely related to the powders and particles used in their formulation. However, there remains a strong need to characterize the impact of particle structures on tablet compac...

Investigation into liquisolid system processability based on the SeDeM Expert System approach.

International journal of pharmaceutics
Liquisolid systems are emerging formulation approach for poorly soluble drugs, based on adsorption/absorption of drug dispersion and obtaining free-flowing powder with good compressibility. SeDeM Expert System represents a powder processability evalu...

SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.

Journal of chemical information and modeling
While accurate prediction of aqueous solubility remains a challenge in drug discovery, machine learning (ML) approaches have become increasingly popular for this task. For instance, in the Second Challenge to Predict Aqueous Solubility (SC2), all gro...