AI Medical Compendium Journal:
Annual review of pharmacology and toxicology

Showing 1 to 8 of 8 articles

Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.

Annual review of pharmacology and toxicology
In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 he...

Artificial Intelligence for Drug Discovery: Are We There Yet?

Annual review of pharmacology and toxicology
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indi...

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes.

Annual review of pharmacology and toxicology
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targe...

Introduction to the Theme "Development of New Drugs: Moving from the Bench to Bedside and Improved Patient Care".

Annual review of pharmacology and toxicology
Investigations in pharmacology and toxicology range from molecular studies to clinical care. Studies in basic and clinical pharmacology and in preclinical and clinical toxicology are all essential in bringing new knowledge and new drugs into clinical...

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

Annual review of pharmacology and toxicology
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of sy...

Big Data and Artificial Intelligence Modeling for Drug Discovery.

Annual review of pharmacology and toxicology
Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynami...

Artificial Intelligence in Drug Treatment.

Annual review of pharmacology and toxicology
The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This...

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Annual review of pharmacology and toxicology
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...