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.

翻译：本研究聚焦于属性定向任务：即根据目标化学性质生成分子，以加速新型药物和材料开发中的候选筛选。DiGress是一种针对分子图的最新扩散模型，其显著特征是通过基于分类器的引导实现属性定向。尽管基于分类器的引导能够生成类分子图，但我们指出其假设在化学领域适用性较差。基于这一发现，我们提出无分类器DiGress（FreeGress），通过将条件信息直接注入训练过程来工作。无分类器引导因其假设条件较宽松且无需训练辅助属性回归器，从而使模型可训练参数减半，具有便利性。实验表明，在QM9和ZINC-250k基准测试的属性定向任务中，我们的模型相比DiGress在平均绝对误差上提升高达79%。作为额外贡献，我们基于某些化学性质（如分子量与分子中原子数的相关性）的观察，提出了一种简单而有效的方法来提升生成样本的化学有效性。