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.

翻译：近年来，基于计算机的分子设计受到了机器学习界的广泛关注。在为制药应用设计新化合物时，通常需要优化这些分子的多个属性：与靶标的结合能、可合成性、毒性、EC50等。虽然以往的方法采用标量化方案将多目标问题转化为偏好条件化的单目标问题，但已证实，当面对具有凹帕累托前沿的问题时，这种简化可能产生倾向于滑向目标空间极值点的解。在本研究中，我们尝试使用目标条件化分子生成的替代方案，以获得更可控的条件模型，从而能够沿整个帕累托前沿均匀探索解空间。