Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
Keywords: Molecular Design, GFlownets, Goal-conditioned RL, Multi-objective Optimisation
TL;DR: We present a goal-conditioned approach which we combine with GFlowNets trained for multi-objective molecular design as a way to more thoroughly cover the set of possible solutions in the objective space.
Abstract: 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.
Submission Number: 3
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