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Multi-objective optimization methods in novel drug design.

George Lambrinidis1, Anna Tsantili-Kakoulidou1

  • 1Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece.

Expert Opinion on Drug Discovery
|December 23, 2020
PubMed
Summary
This summary is machine-generated.

Multi-objective optimization (MOO) is crucial for efficient drug design. Combining MOO techniques, especially with artificial intelligence, enhances decision-making and success rates in drug discovery.

Keywords:
Multi-objective drug designdesirability functionsdominancedrug-likenessmulti-objective optimizationpareto analysissingle objective optimizationweighted sum

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Area of Science:

  • Medicinal Chemistry
  • Molecular Biology
  • Computational Chemistry

Background:

  • Drug design optimization is evolving into a dedicated research field.
  • Current strategies include single-objective optimization (SOO) and multi-objective optimization (MOO).

Purpose of the Study:

  • To review multi-objective optimization (MOO) techniques in drug design.
  • To compare MOO methods, detailing their advantages and limitations.

Main Methods:

  • Focus on Pareto analysis, dominance, Pareto front, and Pareto ranking.
  • Discuss desirability functions and weighted sum approaches for transforming MOO to SOO.
  • Explore integration with evolutionary algorithms and artificial intelligence.

Main Results:

  • Pareto-based methods face challenges in high dimensions and data uncertainty.
  • Combined MOO techniques significantly improve drug design efficiency.
  • Network approaches support optimization for multi-target drug design.

Conclusions:

  • Combined MOO, especially with AI, dramatically aids drug design decisions and success probability.
  • MOO applications extend to drug technology and biological complexity, opening new research avenues.