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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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Structure-Activity Relationships and Drug Design01:28

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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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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Preclinical Development: Overview01:28

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Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
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Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Related Experiment Video

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AI-Driven Enhancements in Drug Screening and Optimization.

Adam Serghini1,2, Stephanie Portelli3, David B Ascher4,5

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Methods in Molecular Biology (Clifton, N.J.)
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Summary

Artificial intelligence (AI) streamlines drug discovery by predicting compound success potential, reducing costly attrition. This study presents in silico tools for small molecule development, enhancing efficiency and cost-effectiveness in the pharmaceutical pipeline.

Keywords:
Computational biologyDrug developmentIn silicoMachine learningPharmacokineticsToxicity

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Area of Science:

  • Pharmaceutical Science
  • Computational Chemistry
  • Drug Discovery

Background:

  • High attrition rates in drug discovery incur billions in annual losses.
  • Ensuring safety, quality, and efficacy across all development phases is critical.
  • Streamlining efforts towards high-potential compounds is key for efficiency.

Purpose of the Study:

  • To present in silico tools for small molecule development.
  • To demonstrate AI applications in enhancing the drug discovery pipeline.
  • To reduce attrition rates and improve cost-effectiveness.

Main Methods:

  • Development of a series of in silico computational tools.
  • Application of AI for preclinical screening and property optimization.
  • Integration of tools within the existing pharmaceutical development workflow.

Main Results:

  • AI tools address biological activity screening and pharmacokinetic optimization.
  • Early toxicity prediction and genetic screening improve therapeutic longevity.
  • In silico tools are designed for seamless integration into current pipelines.

Conclusions:

  • AI offers a powerful approach to mitigate drug discovery attrition.
  • The presented in silico tools enhance efficiency and cost-effectiveness.
  • Implementing these AI solutions can revolutionize small molecule development.