In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound for pharmaceutical applications, there are usually multiple properties of such molecules that need to be optimised: binding energy to the target, synthesizability, toxicity, EC50, and so on. While previous approaches have employed a scalarization scheme to turn the multi-objective problem into a preference-conditioned single objective, it has been established that this kind of reduction may produce solutions that tend to slide towards the extreme points of the objective space when presented with a problem that exhibits a concave Pareto front. In this work we experiment with an alternative formulation of goal-conditioned molecular generation to obtain a more controllable conditional model that can uniformly explore solutions along the entire Pareto front.
@article{arxiv.2306.04620,
title = {Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design},
author = {Julien Roy and Pierre-Luc Bacon and Christopher Pal and Emmanuel Bengio},
journal= {arXiv preprint arXiv:2306.04620},
year = {2023}
}