论文标题
使用条件生成模型生成具有目标性能的新型分子
Generate Novel Molecules With Target Properties Using Conditional Generative Models
论文作者
论文摘要
使用深度学习的药物发现最近引起了很多关注，因为它具有明显的优势，例如效率更高，手动猜测和更快的过程时间。在本文中，我们提出了一个新型的神经网络，用于产生与训练集类似的小分子。我们的网络由一个由BI-GRU层组成的编码器组成，用于将输入样品转换为潜在空间，以增强由1D-CNN层组成的编码器和由Uni-Gru层组成的解码器，以从潜在空间表示中重建样品。潜在空间中的条件矢量用于生成具有所需特性的分子。我们介绍用于培训网络的损失功能，实验细节和财产预测指标。我们的网络使用分子量，logP和对药物类型作为评估指标的定量估计的方法优于先前的方法。
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.
