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Artificial Intelligence in Drug Treatment.

Eden L Romm1, Igor F Tsigelny1,2

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Artificial intelligence (AI) enhances drug treatment by optimizing patient-drug matching, predicting interactions, and refining protocols. This review covers recent AI methods for improved drug selection and administration.

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly applied in drug treatment.
  • Key applications include patient-drug matching, interaction prediction, and protocol optimization.

Purpose of the Study:

  • To review recently developed AI methods for drug treatment and administration.
  • To explain and analyze AI models for efficacy scoring, interaction prediction, and dosage optimization.

Main Methods:

  • Integrating patient data (genetics, proteomics) with drug data (chemical descriptors) for efficacy scoring.
  • Utilizing similarity metrics for predicting drug-target and drug-drug interactions.
  • Employing mathematical models for pharmacokinetic and pharmacodynamic data analysis to optimize dosage.

Main Results:

  • AI facilitates personalized medicine by improving patient-drug selection.
  • AI models accurately predict drug interactions based on molecular similarity.
  • AI optimizes drug administration through pharmacokinetic and pharmacodynamic modeling.

Conclusions:

  • AI offers powerful tools for advancing drug treatment and administration.
  • Recent AI developments significantly enhance precision and efficiency in pharmacotherapy.
  • AI integration promises more effective and personalized patient outcomes.