The structural design of functional molecules, also called molecular
optimization, is an essential chemical science and engineering task with
important applications, such as drug discovery. Deep generative models and
combinatorial optimization methods achieve initial success but still struggle
with directly modeling discrete chemical structures and often heavily rely on
brute-force enumeration. The challenge comes from the discrete and
non-differentiable nature of molecule structures. To address this, we propose
differentiable scaffolding tree (DST) that utilizes a learned knowledge network
to convert discrete chemical structures to locally differentiable ones. DST
enables a gradient-based optimization on a chemical graph structure by
back-propagating the derivatives from the target properties through a graph
neural network (GNN). Our empirical studies show the gradient-based molecular
optimizations are both effective and sample efficient. Furthermore, the learned
graph parameters can also provide an explanation that helps domain experts
understand the model output.

提出差分可伸展树（DST）模型，基于学习知识网络将离散化化学结构转化为局部可微分的结构，并通过图神经网络（GNN）反向传播导数，实现了化学图结构的基于梯度的优化，且优化结果具有有效性和样本效率，并且学习到的图形参数可以提供有助于领域专家理解模型输出的解释。