AIMC Topic: Pharmaceutical Preparations
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Machine learning analysis of molecular dynamics properties influencing drug solubility.
Scientific reports
Jul 24, 2025
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
Jul 17, 2025
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
Jul 2, 2025
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
Jul 2, 2025
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 ...
Improved Prediction of Drug-Protein Interactions through Physics-Based Few-Shot Learning.
Journal of chemical information and modeling
Jun 17, 2025
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
Jun 5, 2025
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
Jun 4, 2025
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
May 30, 2025
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...
Predicting Drug-miRNA Associations Combining SDNE with BiGRU.
IEEE journal of biomedical and health informatics
May 6, 2025
It is well recognized that abnormal miRNA expression can result in drug resistance and pose a challenge to miRNA-based treatments. However, the drug-miRNA associations (DMA) are still incompletely understood. Conventional biological experiments have ...
The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions.
Scientific data
Apr 25, 2025
The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken fr...
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