The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.
复制标题
DOI：
10.18632/oncotarget.14073
复制
发表时间：
2017-02-14
期刊：
影响因子：
--
通讯作者：
Zhavoronkov A
中科院分区：
文献类型：
--
作者：
Kadurin A;Aliper A;Kazennov A;Mamoshina P;Vanhaelen Q;Khrabrov K;Zhavoronkov A
深度学习，特别是生成对抗网络的最新进展，即使使用自然语言作为输入，也能根据请求生成新图像和视频，并显示出令人惊讶的结果。在本文中，我们提出了生成对抗性自动编码器（AAE）的首次应用，用于生成具有一组定义的参数的新型分子指纹。我们开发了一个 7 层 AAE 架构，其中潜在的中间层充当鉴别器。 AAE 使用二进制指纹和分子浓度的向量作为输入和输出。在潜在层中，我们还引入了一个负责生长抑制百分比的神经元，当该百分比为负时，表明治疗后肿瘤细胞数量减少。为了训练 AAE，我们使用了在 MCF-7 细胞系上分析的 6252 种化合物的 NCI-60 细胞系测定数据。 AAE 的输出用于在 PubChem 中筛选 7200 万种化合物，并选择具有潜在抗癌特性的候选分子。这种方法是人工智能药物发现引擎的概念证明，其中 AAE 用于生成具有所需分子特性的新分子指纹。
Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.