论文标题
生成网络复合物，用于自动产生类似药物的分子
Generative network complex for the automated generation of druglike molecules
论文作者
论文摘要
当前的药物发现是昂贵且耗时的。创建具有理想的药理特性并便宜的低收入人士可用的各种新型化合物仍然是一项艰巨的任务。在这项工作中，我们开发了一种生成网络复合物（GNC），以通过自动编码器的潜在空间中的梯度下降来基于多毛皮优化生成新的药物样分子。在我们的GNC中，优化了多种化学特性和相似性评分，以产生和预测具有所需化学特性的药物样分子。为了进一步验证预测的可靠性，这些分子被基于2D指纹的预测指标重新评估和筛选，以提出数百种新药候选者。作为演示，我们将我们的GNC应用于产生大量新的BACE1抑制剂，以及成千上万种现有市场药物的新型替代药物候选者，包括Ceritinib，Ribociclib，ribociclib，acalabrutinib，Idelalalisib，Dabrafenib，Dabrafenib，Macimorelin，Enzalutamide和Panobinamide和Panobinostat。
Current drug discovery is expensive and time-consuming. It remains a challenging task to create a wide variety of novel compounds with desirable pharmacological properties and cheaply available to low-income people. In this work, we develop a generative network complex (GNC) to generate new drug-like molecules based on the multi-property optimization via the gradient descent in the latent space of an autoencoder. In our GNC, both multiple chemical properties and similarity scores are optimized to generate and predict drug-like molecules with desired chemical properties. To further validate the reliability of the predictions, these molecules are reevaluated and screened by independent 2D fingerprint-based predictors to come up with a few hundreds of new drug candidates. As a demonstration, we apply our GNC to generate a large number of new BACE1 inhibitors, as well as thousands of novel alternative drug candidates for eight existing market drugs, including Ceritinib, Ribociclib, Acalabrutinib, Idelalisib, Dabrafenib, Macimorelin, Enzalutamide, and Panobinostat.
