Multiobjective Reinforcement Learning in Optimized Drug Design
Maryam Abbasi, Tiago Pereira, Beatriz P. Santos, Bernardete Ribeiro, Joel Perdiz Arrais · ESANN 2021 proceedings · 2021
Machine learning has been increasingly applied with success in generating synthetically reasonable molecules.However, a complete system capable of both producing valid molecules and optimizing multiple traits has remained elusive.This paper employs multiobjective reinforcement learning to draw a framework to design compounds.Different multiobjective techniques have been evaluated, such as weighted sum and Chebyshev.The results show that the implemented model can be effectively optimized towards different and competing molecular properties.Nonetheless, the model implemented with the weighted sum scalarization technique with a weight of 0.55 for biological affinity is the one with the most appropriate trade-off for the different evaluated properties.