AIMC Topic: Pharmaceutical Preparations

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Evaluation, correction and masking methods for unpleasant tastes of drugs: A comprehensive review.

International journal of pharmaceutics
The unpleasant tastes of drugs, including bitterness, pungency, astringency, and sourness, significantly impedes patient adherence, particularly among pediatric and geriatric populations. Accurate evaluation of these tastes is essential for optimizin...

Machine learning analysis of molecular dynamics properties influencing drug solubility.

Scientific reports
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource cons...

Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing.

Scientific reports
The development of continuous pharmaceutical manufacturing is crucial and can be analyzed via advanced computational models. Machine learning is a strong computational paradigm that can be integrated into a continuous process to enhance the drugs' so...

DCGCN: Dual-Channel Graph Convolutional Network-Based Drug-Target Interaction Prediction Method with 3D Molecular Structure.

Journal of chemical information and modeling
Exploring drug-target interactions (DTIs) is crucial for drug discovery. Most existing methods for predicting DTIs rely solely on the linear structures of molecules, such as SMILES or the amino acid sequence. However, these linear features fail to re...

Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations.

Scientific reports
This study introduces a sophisticated predictive framework for determining drug solubility and activity values in formulations via machine learning. The framework utilizes a comprehensive dataset consisting of more than 12,000 data rows and 24 input ...

Ranking of apparent drug affinity to mesoporous silica utilizing a chromatographic screening method and a tree-based prediction model.

International journal of pharmaceutics
Mesoporous silica has emerged as a promising component in bio-enabling formulation strategy. However, there is currently a lack of predictive tools for assessing drug-silica interactions in a preformulation phase, when formulators only have minimal m...

Improved Prediction of Drug-Protein Interactions through Physics-Based Few-Shot Learning.

Journal of chemical information and modeling
Accurate prediction of drug-protein interactions is crucial for drug discovery. Due to the bottleneck of traditional scoring functions, many machine learning scoring functions (MLSFs) have been proposed for structure-based drug screening. However, ex...

Machine Learning Based Quantitative Structure-Dissolution Profile Relationship.

Journal of chemical information and modeling
Determining accurate drug dissolution processes in the gastrointestinal tract is critical in drug discovery as dissolution profiles provide essential information for estimating the bioavailability of orally administered drugs. While various methods h...

Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models.

Scientific reports
For analysis of crystallization, the solubility of drug in solvents should be correlated to input parameters. In this investigation, the solubility of salicylic acid as drug model in a variety of solvents is predicted through the utilization of multi...

Prediction of drug-target interactions based on substructure subsequences and cross-public attention mechanism.

PloS one
Drug-target interactions (DTIs) play a critical role in drug discovery and repurposing. Deep learning-based methods for predicting drug-target interactions are more efficient than wet-lab experiments. The extraction of original and substructural feat...