Transform Drug Discovery with Machine Learning and AI

BIOVIA Generative Therapeutics Design (GTD) is an agile cloud-based solution that improves lead optimization. GTD combines advanced data science, machine learning, cheminformatics and structure-based modeling to explore chemical space and automate the virtual creation, testing and selection of novel compounds, identifying high-quality small molecule drug candidates faster.

Combine Virtual Models with Real Data

BIOVIA Generative Therapeutics Design combines Virtual and Real (V+R) lead optimization to support the “active learning” innovation cycle:

  • Virtual cycles help explore chemical space by “learning” from real experiments. The system virtually screens and optimizes candidate compounds using a combination of Machine Learning models and structure-based modeling and simulation methods. Multi-parameter optimization algorithms balance competing objectives, including pharmacokinetics, pharmacodynamics, ADME (absorption, distribution, metabolism, excretion), bioavailability, drug metabolism, and more.
  • Real cycles uses the experimental data collected from the synthesis and testing of the most promising virtual compounds in the lab. This data is then used to improve predictive models and refine the exploration of chemical space.
  • These V+R active learning cycles continue until the identification of compounds with the desired target product profile (TPP). 

Benefits of Generative Therapeutics Design

  • Investigate a diverse set of compounds but spend less time running experiments to optimize leads
  • Shorten lead optimization phase for drug candidates
  • Increase success rate in preclinical and clinical studies
  • Deploy an agile, secure and cloud-based solution with low total cost of ownership
  • Consider existing intellectual property in the design process
  • Save millions in research dollars
BIOVIA Generative Therapeutics Design > Dassault Systemes

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