Computer-based de-novo design of functional molecules is one of the most prominent challenges in cheminformatics today. As a result, generative and evolutionary inverse designs from the field of artificial intelligence have emerged at a rapid pace, with aims to optimize molecules for a particular chemical property. These models ‘indirectly’ explore the chemical space; by learning latent spaces, policies, and distributions, or by applying mutations on populations of molecules. However, the recent development of the SELFIES (Krenn 2020 Mach. Learn.: Sci. Technol. 1 045024) string representation of molecules, a surjective alternative to SMILES, have made possible other potential techniques. Based on SELFIES, we therefore propose PASITHEA, a direct gradient-based molecule optimization that applies inceptionism (Mordvintsev 2015) techniques from computer vision. PASITHEA exploits the use of gradients by directly reversing the learning process of a neural network, which is trained to predict real-valued chemical properties. Effectively, this forms an inverse regression model, which is capable of generating molecular variants optimized for a certain property. Although our results are preliminary, we observe a shift in distribution of a chosen property during inverse-training, a clear indication of PASITHEA’s viability. A striking property of inceptionism is that we can directly probe the model’s understanding of the chemical space on which it is trained. We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.

基于计算机的功能分子从头设计是当今化学信息学中最突出的挑战之一。因此，人工智能领域的生成和进化逆向设计迅速出现，旨在针对特定化学性质优化分子。这些模型“间接”探索化学空间；通过学习潜在空间、策略和分布，或通过对分子种群应用突变。然而，SELFIES (Krenn 2020 Mach. Learn.: Sci. Technol. 1045024) 分子的字符串表示，SMILES 的一种外射替代，使其他潜在技术成为可能。因此，基于 SELFIES，我们提出了 PASITHEA，这是一种直接基于梯度的分子优化，它应用了计算机视觉中的 inceptionism (Mordvintsev 2015) 技术。PASITHEA 通过直接反转神经网络的学习过程来利用梯度的使用，神经网络经过训练可以预测实值化学性质。实际上，这形成了一个逆回归模型，该模型能够生成针对特定属性优化的分子变体。尽管我们的结果是初步的，但我们观察到在反向训练期间所选属性的分布发生了变化，这清楚地表明了 PASITHEA 的可行性。Inceptionism 的一个显着特性是我们可以直接探测模型的了解它所训练的化学空间。我们预计将 PASITHEA 扩展到更大的数据集、分子和更复杂的属性将导致新功能分子的设计以及机器学习模型的解释和解释方面的进步。