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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Artificial intelligence in multi-objective drug design.

Sohvi Luukkonen1, Helle W van den Maagdenberg2, Michael T M Emmerich3

  • 1Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden, 2333 CC, the Netherlands. Electronic address: https://twitter.com/sohvi_luukkonen.

Current Opinion in Structural Biology
|February 12, 2023
PubMed
Summary
This summary is machine-generated.

Designing new drugs is complex, involving multiple objectives. This study reviews artificial intelligence methods, including population-based metaheuristics, deep reinforcement learning, and conditional learning, for multi-objective compound design.

Keywords:
Compound optimisationMulti-objective optimisationPareto dominancede novo drug design

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Artificial intelligence

Background:

  • Drug discovery is a complex, multi-objective optimization problem.
  • Machine learning and optimization methods have accelerated multi-objective compound design.
  • Traditional methods often rely on aggregation functions or Pareto-based strategies.

Purpose of the Study:

  • To provide an overview of artificial intelligence methods used in multi-objective drug design.
  • To highlight recent innovations and emerging techniques in the field.
  • To discuss the application of various optimization strategies in compound design.

Main Methods:

  • Review of population-based metaheuristics.
  • Analysis of deep reinforcement learning applications.
  • Exploration of conditional learning methods in drug design.
  • Discussion of Pareto-based and aggregation function strategies.

Main Results:

  • Population-based metaheuristics and deep reinforcement learning are prevalent AI methods.
  • Conditional learning methods are emerging as a popular approach.
  • Various innovative strategies are being developed to address multi-objective optimization challenges.
  • The field is rapidly advancing with new developments and applications.

Conclusions:

  • Multi-objective optimization is central to de novo drug design.
  • AI, particularly deep learning and conditional learning, is transforming compound design.
  • Continued innovation in optimization strategies is crucial for successful drug discovery.