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Challenges with multi-objective QSAR in drug discovery.

George Lambrinidis1, Anna Tsantili-Kakoulidou1

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

Expert Opinion on Drug Discovery
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Summary
This summary is machine-generated.

Quantitative structure-activity relationship (QSAR) models are evolving to meet multi-objective drug discovery challenges. New methods like single-objective optimization (SOO) and multi-objective optimization (MOO) aid in developing efficient and safer drugs.

Keywords:
QSARdrug-like spacemulti-objective drug designmulti-objective optimizationmulti-target drug designsingle objective optimization

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Medicinal chemistry

Background:

  • The drug discovery pipeline faces complexity due to vast experimental and computational data.
  • Technological advancements and large datasets necessitate the evolution of Quantitative Structure-Activity Relationships (QSAR).
  • Multi-objective drug discovery presents significant challenges that QSAR methodologies must address.

Purpose of the Study:

  • To review the evolution and transformation of QSAR in the context of multi-objective drug discovery.
  • To discuss the definition of drug-like space and the construction of models for single-objective optimization (SOO).
  • To explore multi-objective optimization (MOO) strategies, including desirability functions and Pareto surfaces, for QSAR applications.

Main Methods:

  • Overview of multiple objectives in drug discovery.
  • Focus on defining drug-like space and constructing local/global models.
  • Discussion of platforms and workflows for step-by-step SOO.
  • Exploration of MOO applications in QSAR for multi-target design and polypharmacology.

Main Results:

  • QSAR models are adapting to handle multiple, often conflicting, optimization objectives.
  • Single-objective optimization (SOO) via web platforms and workflows enables sequential optimization for various endpoints.
  • Multi-objective optimization (MOO) is valuable for multi-target or selectivity design, and model prioritization, though currently limited in scope.

Conclusions:

  • Cloud technology facilitates access to large databases and software services, accelerating research for efficient and safer drugs.
  • Integrated QSAR models in platforms and workflows support sequential SOO for biological and toxicity endpoints.
  • MOO offers a powerful alternative optimization philosophy for complex drug design challenges, particularly in multi-target scenarios.