AIMC Topic: Chemistry, Pharmaceutical
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Data Scaling and Generalization Insights for Medicinal Chemistry Deep Learning Models.
Journal of chemical information and modeling
Jun 2, 2025
Predictive models hold considerable promise in enabling the faster discovery of safer, more efficacious therapeutics. To better understand and improve the performance of small-molecule predictive models for drug discovery, we conduct multiple experim...
Application of rheology to hot melt extrusion: Theory and practice.
International journal of pharmaceutics
May 29, 2025
Hot melt extrusion (HME) has become a key manufacturing method in the pharmaceutical industry for developing novel drug delivery systems, due to its solvent-free nature, ease of operation, and ability to achieve one-step molding and continuous proces...
Development of machine learning models for estimation of disintegration time on fast-disintegrating tablets.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
May 23, 2025
The disintegration time for solid dosage oral formulations is directly influenced by diverse factors such as molecular properties, physical characteristics, excipient compositions, and formulation-specific attributes. This research addresses the chal...
PLGA-based long-acting injectable (LAI) formulations.
Journal of controlled release : official journal of the Controlled Release Society
Apr 21, 2025
Long-acting injectable (LAI) formulations, which deliver drugs over weeks or months, have been in use for more than three decades. Most clinically approved LAI products are formulated using poly(lactide-co-glycolide) (PLGA) polymers. Historically, th...
Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation.
Scientific reports
Apr 21, 2025
This research assesses multiple predictive models aimed at estimating disintegration time for pharmaceutical oral formulations, based on a dataset comprising nearly 2,000 data points that include molecular, physical, compositional, and formulation at...
Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.
Pharmaceutical research
Apr 17, 2025
PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, id...
Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.
Pharmaceutical research
Apr 3, 2025
OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. ...
AI-Augmented R-Group Exploration in Medicinal Chemistry.
Journal of chemical information and modeling
Feb 17, 2025
Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding R-groups by atom-centric pharmaco...
Scale-independent solid fraction prediction in dry granulation process using a gray-box model integrating machine learning model and Johanson model.
International journal of pharmaceutics
Feb 15, 2025
We propose a novel approach for predicting the solid fraction after roller compaction processes. It is crucial to predict and control the solid fraction, as it has a significant impact on the product quality. The solid fraction can be theoretically p...
Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.
AAPS PharmSciTech
Feb 3, 2025
Lipid-based formulations are essential for enhancing drug solubility and bioavailability, yet selecting optimal lipid excipients for specific drugs remains challenging. This study introduces Sol_ME, a machine learning-based model designed to predict ...
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