Artificial intelligence in multi-objective drug design.

Journal: Current opinion in structural biology
Published Date:

Abstract

The factors determining a drug's success are manifold, making de novo drug design an inherently multi-objective optimisation (MOO) problem. With the advent of machine learning and optimisation methods, the field of multi-objective compound design has seen a rapid increase in developments and applications. Population-based metaheuris-tics and deep reinforcement learning are the most commonly used artificial intelligence methods in the field, but recently conditional learning methods are gaining popularity. The former approaches are coupled with a MOO strat-egy which is most commonly an aggregation function, but Pareto-based strategies are widespread too. Besides these and conditional learning, various innovative approaches to tackle MOO in drug design have been proposed. Here we provide a brief overview of the field and the latest innovations.

Authors

  • Sohvi Luukkonen
    ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, A-4040 Linz, Austria.
  • Helle W van den Maagdenberg
    Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden, 2333 CC, the Netherlands.
  • Michael T M Emmerich
    Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, Leiden, 2333 CC, the Netherlands.
  • Gerard J P van Westen
    Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands. Electronic address: gerard@lacdr.leidenuniv.nl.