AIMC Topic: Chemistry, Pharmaceutical

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Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.

Pharmaceutical research
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
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
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
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
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 ...

Artificial Intelligence in Retrosynthesis Prediction and its Applications in Medicinal Chemistry.

Journal of medicinal chemistry
Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space...

Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging.

Journal of pharmaceutical and biomedical analysis
Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control method...

'Applications of machine learning in liposomal formulation and development'.

Pharmaceutical development and technology
Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algo...

A deep learning approach to perform defect classification of freeze-dried product.

International journal of pharmaceutics
Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visual inspection, this method poses typical challenges and shortcomings that can be addressed with innov...

AI-directed formulation strategy design initiates rational drug development.

Journal of controlled release : official journal of the Controlled Release Society
Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on limited personal experiences to perform trial-and-error tests on diverse formulation strategies. Such ...