Artificial intelligence in multi-objective drug design

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. [Display omitted] •More AI methods in multi-objective drug design, including metaheuristics, reinforcement and conditional deep learning models.•Aggregation of objectives and Pareto ranking approaches are the most widely used multi-objective optimisation methods.•Important future trends in AI-based drug discovery are identified, e.g. scalability in terms of the number of objectives.•To consolidate the field, further benchmarking and experimental validation of models are needed.