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Related Concept Videos

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System05:10

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Antipsychotic drugs primarily block dopamine and serotonin receptors and cholinergic, adrenergic, and histaminergic receptors, thereby reducing hallucinations and delusions in conditions like schizophrenia. However, they can trigger unwanted extrapyramidal effects such as dystonias, Parkinson-like symptoms, and tardive dyskinesia.
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Related Experiment Video

Updated: Jan 20, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.2K

Deep learning in stroke therapeutics: drug repurposing and beyond.

Kit-Kay Mak1, Bharath Chelluboina1

  • 1Department of Pharmacy Practice, University of Illinois Chicago, Chicago, IL, USA.

Expert Opinion on Drug Discovery
|January 19, 2026
PubMed
Summary

Deep learning (DL) accelerates stroke drug repurposing by analyzing complex data, overcoming research challenges. While AI shows promise in stroke research and clinical tools, issues like interpretability and validation need addressing.

Keywords:
Artificial intelligencedeep learningdrug repositioningdrug repurposingstroke

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Related Experiment Videos

Last Updated: Jan 20, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Published on: December 11, 2016

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Pharmacology

Background:

  • Stroke presents a significant global health burden with limited effective therapeutic options.
  • Heterogeneous pathology, narrow therapeutic windows, and poor translation hinder conventional stroke drug development.
  • Deep learning (DL) offers novel computational approaches to address these challenges.

Purpose of the Study:

  • To review the application of DL in preclinical and clinical stroke research.
  • To emphasize DL's role in drug discovery and repurposing for stroke.
  • To discuss current limitations and future directions of DL in stroke research.

Main Methods:

  • A narrative review of peer-reviewed studies.
  • Literature search on PubMed using keywords: drug repurposing, stroke, computational approaches (2020-2025).

Main Results:

  • DL accelerates drug repurposing and development for stroke by analyzing high-dimensional data.
  • DL aids in target identification, virtual screening, and bridging translational gaps.
  • Regulatory approvals for AI-based diagnostic tools indicate growing clinical adoption.

Conclusions:

  • DL is a powerful tool for advancing stroke research and therapy development.
  • Addressing challenges in model interpretability, generalizability, and validation is crucial for clinical translation.
  • Continued research and development in DL are essential for improving stroke outcomes.