Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
This work addresses the need for more controllable and uniform molecular generation in pharmaceutical applications, representing an incremental improvement over existing methods.
The paper tackled the problem of multi-objective molecular design by proposing a goal-conditioned GFlowNets approach to uniformly explore solutions along the entire Pareto front, addressing issues with previous scalarization methods that produce extreme solutions.
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.