This work focuses on the task of property targeting: that is, generating molecules conditioned on target chemical properties to expedite candidate screening for novel drug and materials development. DiGress is a recent diffusion model for molecular graphs whose distinctive feature is allowing property targeting through classifier-based (CB) guidance. While CB guidance may work to generate molecular-like graphs, we hint at the fact that its assumptions apply poorly to the chemical domain. Based on this insight we propose a classifier-free DiGress (FreeGress), which works by directly injecting the conditioning information into the training process. CF guidance is convenient given its less stringent assumptions and since it does not require to train an auxiliary property regressor, thus halving the number of trainable parameters in the model. We empirically show that our model yields up to 79% improvement in Mean Absolute Error with respect to DiGress on property targeting tasks on QM9 and ZINC-250k benchmarks. As an additional contribution, we propose a simple yet powerful approach to improve chemical validity of generated samples, based on the observation that certain chemical properties such as molecular weight correlate with the number of atoms in molecules.

本研究旨在通过无监督学习直接注入条件信息，提出一种无分类器指导的DiGress （FreeGress）模型，用于加速新药和材料开发过程中的候选筛选，实验证明该模型在目标性质预测任务上比DiGress模型在QM9和ZINC-250k基准测试中取得了79％的绝对误差改进，并提出了一种基于分子量与原子数的化学有效性改进方法。

基于图传播的无分类器分子性质定向策略