Drug discovery using deep learning has attracted a lot of attention of late as it has obvious advantages like higher efficiency, less manual guessing and faster process time. In this paper, we present a novel neural network for generating small molecules similar to the ones in the training set. Our network consists of an encoder made up of bi-GRU layers for converting the input samples to a latent space, predictor for enhancing the capability of encoder made up of 1D-CNN layers and a decoder comprised of uni-GRU layers for reconstructing the samples from the latent space representation. Condition vector in latent space is used for generating molecules with the desired properties. We present the loss functions used for training our network, experimental details and property prediction metrics. Our network outperforms previous methods using Molecular weight, LogP and Quantitative Estimation of Drug-likeness as the evaluation metrics.

翻译：使用深层学习的药物发现最近引起了许多关注,因为它有明显的好处,例如效率更高、人工猜测较少、过程时间更快等。在本文中,我们展示了一个新的神经网络,用于产生与培训组中小分子类似的小分子。我们的网络包括一个由双GRU层组成的编码器,用于将输入样本转换为潜质空间,用于提高由1D-CNN层组成的编码器能力的预测器,以及由单-GRU层组成的从潜在空间代表层中重建样品的解码器。潜空中的条件矢量器被用于生成具有预期特性的分子。我们展示了用于培训我们的网络、实验细节和财产预测指标的丢失功能。我们的网络利用分子重量、LogP和药物类定量模拟作为评估指标,超越了以往的方法。