ReviewKeynoteFrom flamingo dance to (desirable) drug discovery: a nature-inspired approach

- We approach drug discovery as a nature-inspired multi-criteria optimization process.
- We propose a desirability-based method for multi-criteria virtual screening.
- We highlight the role of ensemble modeling and the applicability domain.
- We provide evidences of the suitability of the method through two case studies.

The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.

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