Multi-objective optimization methods in novel drug design

ABSTRACT

Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single – objective optimization (SOO) and multi-objective optimization (MOO).

Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.

Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

KEYWORDS:

• Multi-objective drug design
• single objective optimization
• multi-objective optimization
• pareto analysis
• dominance
• desirability functions
• weighted sum
• drug-likeness

Article highlights

• Drug discovery is a complex process, with drug candidates being the outcome of multiple and often conflicting objectives

• Optimization has gained particular importance in multi-objective drug discovery, being upgraded to a discipline that attracts own research.

• Optimization strategies can be broadly classified to single –objective (SOO) and multi-objective optimization (MOO), although there are no distinct borders between them.

• SOO leads to a single solution, while in MOO there is no unique solution but there are trade-offs among the objectives, leading to a family of solutions

• Pareto analysis and the concept of dominance stand in the core of MOO techniques.

• A MOO problem can be transformed to SOO by different approaches, such as desirability functions and the weighted sum method.

• High dimensions and uncertainty in available data are challenges to be encountered in MOO.

• The use of combined MOO techniques, as well as complementary to SOO or in conjunction with artificial intelligence, contributes dramatically in efficient drug design, assisting decisions and increasing success probabilities.

• Applicability of MOO to other fields like drug technology and biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

This box summarizes key points contained in the article.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

This manuscript was not funded.