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Related Concept Videos

Drug Discovery: Overview01:26

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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...
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Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
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Prodrugs are a class of pharmaceutical compounds that undergo a biotransformation process within the body to be converted into a pharmacologically active drug. Prodrugs are designed to improve the therapeutic properties of the parent drug, such as enhancing bioavailability, increasing stability, or reducing toxicity. The concept of prodrugs revolves around modifying the chemical structure of the original drug to make it more effective or convenient for administration.
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From Prompt to Drug: Toward Pharmaceutical Superintelligence.

Alex Zhavoronkov1,2,3, David Gennert1, Jiye Shi4

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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) and automated labs enable a "prompt-to-drug" pipeline for autonomous drug discovery. This approach promises scalable, efficient, and unbiased development from hypothesis to clinical planning.

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Area of Science:

  • Pharmacology
  • Artificial Intelligence
  • Laboratory Automation

Background:

  • Generative artificial intelligence (AI) platforms are rapidly evolving.
  • Automated laboratory systems offer precise and reproducible experimental execution.
  • The integration of these technologies presents a novel opportunity in pharmaceutical research.

Purpose of the Study:

  • To explore the convergence of generative AI and automated laboratory systems.
  • To present a "prompt-to-drug" pipeline for autonomous drug discovery.
  • To outline a vision for scalable, efficient, and unbiased drug development.

Main Methods:

  • Review of recent advancements in AI technologies for drug discovery.
  • Description of a "prompt-to-drug" pipeline integrating AI and automation.
  • Highlighting key breakthroughs and case studies in AI-driven drug development.
  • Discussion of the required technological infrastructure for closed-loop systems.

Main Results:

  • AI can generate novel hypotheses and design optimized drug candidates.
  • AI can orchestrate synthesis, validation, and clinical planning.
  • The "prompt-to-drug" pipeline enables a fully autonomous, end-to-end drug development program.
  • Key breakthroughs demonstrate the feasibility of AI-driven autonomous drug discovery.

Conclusions:

  • The convergence of generative AI and automation marks a new era in drug discovery.
  • This paradigm shift enables a scalable, efficient, and unbiased approach to developing therapeutics.
  • The "prompt-to-drug" pipeline offers a vision for the future of pharmaceutical research and development.