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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Preclinical Development: Overview01:28

Preclinical Development: Overview

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Biopharmaceutics and Pharmacokinetics: Overview

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Related Experiment Video

Updated: May 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

AI-powered digital innovations in pharmaceuticals research & development: Current landscape and case examples.

Xiao Li1, Jian Dai2, Herbert Pang2

  • 1Computational Science and Informatics, Roche Diagnostics Solutions, Santa Clara, CA, USA.

Journal of Biopharmaceutical Statistics
|May 19, 2026
PubMed
Summary

Artificial intelligence (AI) is transforming pharmaceutical R&D. This paper explores AI trends, including Large Language Models (LLMs) and spatial omics, offering insights for statisticians and data scientists.

Keywords:
GenAILLMspharmaceutical industryspatial omics

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Last Updated: May 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

Area of Science:

  • Pharmaceutical industry
  • Computational biology
  • Data science

Background:

  • AI and machine learning (ML) are increasingly vital in pharmaceutical research and development.
  • Emerging AI technologies like Large Language Models (LLMs) and spatial omics present new opportunities.
  • Understanding current AI trends is crucial for statisticians and data scientists in the pharmaceutical sector.

Purpose of the Study:

  • To examine current trends in AI-powered digital innovations in the pharmaceutical industry.
  • To focus on the challenges and opportunities presented by LLMs, Generative AI (GenAI), and ML for spatial omics.
  • To provide an overview for integrating AI into pharmaceutical R&D and inspire future applications.

Main Methods:

  • Review of recent applications of AI technologies by leading pharmaceutical companies.
  • Analysis of challenges and opportunities associated with AI adoption.
  • Inclusion of case examples illustrating clinical and operational utilities of AI.
  • Brief mention of regulatory guidance on AI-driven tools in drugs and devices.

Main Results:

  • AI adoption in pharmaceuticals offers significant potential benefits alongside inherent challenges.
  • Case examples demonstrate AI's utility in high-plex tissue image analysis for single-cell segmentation and spatial pattern discovery.
  • Retrieval Augmented Generation (RAG) using LLMs shows promise for standardizing clinical trial monitoring.

Conclusions:

  • AI integration, particularly LLMs and spatial omics, is reshaping pharmaceutical R&D.
  • Statisticians and data scientists can leverage these insights for current R&D and future AI applications.
  • The paper provides a valuable overview for navigating AI advancements in the pharmaceutical landscape.