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AI-powered programmable virtual humans toward human physiologically-based drug discovery.

You Wu1, Philip E Bourne2, Lei Xie3

  • 1School of Pharmacy and Pharmaceutical Sciences & Center for Drug Discovery, Northeastern University, Boston, MA, USA; Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.

Drug Discovery Today
|October 10, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enables virtual human models for drug discovery, predicting compound efficacy and safety. This new paradigm moves beyond digitizing experiments to testing novel drugs in silico for earlier optimization.

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Drug Discovery

Background:

  • Current artificial intelligence (AI) in drug discovery digitizes experiments but fails to predict clinical outcomes.
  • Pharmacology digital twins are limited to late-phase development and cannot bridge early-stage translational gaps.
  • AI's true potential involves virtual experiments for novel compound testing within a human model.

Purpose of the Study:

  • To introduce a new paradigm in physiologically-based drug discovery using AI-driven virtual humans.
  • To enable early-stage evaluation and optimization of compound efficacy and safety.
  • To leverage advances in AI and omics for in silico drug testing.

Main Methods:

  • Development of dynamic, multiscale models representing programmable virtual humans.
  • Integration of artificial intelligence (AI), high-throughput assays, and single-cell and spatial omics.
  • Creation of physiologically-based models for in silico compound evaluation.

Main Results:

  • Establishment of a new paradigm for physiologically-based drug discovery.
  • Enabling virtual experiments to test novel compounds within a human model.
  • Facilitating earlier assessment of compound efficacy and safety.

Conclusions:

  • AI-powered virtual humans represent a transformative approach to drug discovery.
  • This method allows for in silico testing, overcoming limitations of current experimental digitization.
  • The approach promises to optimize compound efficacy and safety earlier in the drug development pipeline.