AIMC Topic: Chemistry, Pharmaceutical

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Machine learning-driven discovery of multicomponent pharmaceutical solid forms via DualNet: confidence-aware prediction and ranking of salts and cocrystals.

International journal of pharmaceutics
Salts and cocrystals are vital multicomponent entities for tuning pharmaceuticals' solid-state properties, yet their experimental screening is labor-intensive and often inefficient. We introduce a DualNet Ensemble algorithm, a multi-class classificat...

Predictive modeling approach using machine learning-integrated design of experiments in quality by design for optimizing resveratrol-loaded polymeric nanoparticle formulation.

International journal of pharmaceutics
This study aimed to explore the potential of a Machine learning (ML)-integrated Quality by design (QbD) process to formulate resveratrol (RES)-loaded polymeric nanoparticles (RES-PNPs) for potential use in transdermal drug delivery. The RES-PNPs were...

Ever-Increasing Role of Computational Tools in Solid-State Pharmaceutics: Advancing Drug Development with Enhanced Molecular Understanding and Risk Assessment.

Molecular pharmaceutics
The field of solid-state pharmaceutics comprises a broad range of investigations into various structural aspects of pharmaceutical solids, establishing a rational structure-property correlation. These solid systems allow the tunability of the physico...

Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.

The AAPS journal
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...

Nebulized liposomal drug delivery: a SWOT analysis in drug development.

International journal of pharmaceutics
This critical review provided a detailed strengths, weaknesses, opportunities, and threats (SWOT) analysis of nebulized liposomal formulations, focusing on the strengths and translational potential. The formulations have notable strengths, such as en...

Recent advances in dry powder inhalation formulations prepared by co-spray drying technology: a comprehensive review.

International journal of pharmaceutics
Co-spray drying technology represents an increasingly important approach in preparing dry powder inhalation (DPI) formulations. Compared to conventional spray drying, co-spray drying typically yields particles characterized by improved aerosol perfor...

Data Scaling and Generalization Insights for Medicinal Chemistry Deep Learning Models.

Journal of chemical information and modeling
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...

PLGA-based long-acting injectable (LAI) formulations.

Journal of controlled release : official journal of the Controlled Release Society
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
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...