AIMC Topic: Drug Development

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Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML.

Journal of chemical information and modeling
The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate. Different techniques are available to address the class i...

Artificial Intelligence for Clinical Trial Facilitation, Lessons for Inflammatory Bowel Disease: A Scoping Review.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND & AIMS: Despite major advances in many fields of science and technology, pharmaceutical research and development continues to be inefficient across multiple diseases. Inflammatory bowel disease (IBD) trials are also subject to high failure...

AI drug development's data problem.

Science (New York, N.Y.)
The future of drug discovery may be artificial intelligence (AI), but its present is not. AI is in its infancy in the field. To help AI mature, developers need nonproprietary, open, large, high-quality datasets to train and validate models, managed b...

Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.

CPT: pharmacometrics & systems pharmacology
With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed ('true' or 'real') sources and artificial data ...

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. ...

The reality of modeling irritable bowel syndrome: progress and challenges.

Expert opinion on drug discovery
INTRODUCTION: Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is often therapeutically challenging. While research has advanced our understanding of IBS pathophysiology, developing precise models to predict drug response and...

Advances in AI-based strategies and tools to facilitate natural product and drug development.

Critical reviews in biotechnology
Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly ...

Embracing the changes and challenges with modern early drug discovery.

Expert opinion on drug discovery
INTRODUCTION: The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery ...

Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review.

Drug discovery today
Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review discus...

Applications of Artificial Intelligence in Drug Repurposing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass of early s...