Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.

中文翻译：

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一种基于潜在载体的生成对抗网络的从头分子生成方法

应用于药物发现的深度学习方法已用于生成新颖的结构。在这项研究中，我们提出了一种新的深度学习架构LatentGAN，该架构结合了自动编码器和生成对抗性神经网络以进行从头分子设计。我们在两种情况下应用了该方法：一种生成随机的类药物化合物，另一种生成目标偏倚的化合物。我们的结果表明，该方法在两种情况下均有效。从训练后的模型中采样的化合物可以在很大程度上占用与训练集相同的化学空间，并且还可以生成相当一部分新颖的化合物。此外，从LatentGAN采样的化合物的药物相似性评分也与训练集相似。最后，生成的化合物与通过基于递归神经网络的生成模型方法获得的化合物不同，