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Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
Julien Roy · Pierre-Luc Bacon · Christopher Pal · Emmanuel Bengio

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in Challenges in Deployable Generative AI »
Event URL: https://openreview.net/forum?id=ghNQhtCHD2 »

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.

Keywords: GFlowNets · Molecular Design · Goal-conditioned RL · Multi-objective Optimisation

Author Information

Julien Roy (Mila)
Pierre-Luc Bacon (University of Montreal)
Christopher Pal (MILA)
Emmanuel Bengio (McGill University)

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