  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.
