Multi-objective de novo drug design with conditional graph generative model.
Multi-objective de novo drug design with conditional graph generative model.
复制标题
具有条件图生成模型的多目标从头药物设计
DOI：
10.1186/s13321-018-0287-6
复制
发表时间：
2018-07-24
影响因子：
8.6
通讯作者：
Liu Z
中科院分区：
文献类型：
--
作者：
Li Y;Zhang L;Liu Z
最近，深度生成模型已经被证明是进行从头分子设计的一种很有前途的方法。然而，以前的研究主要集中在生成SMILES字符串，而不是分子图。虽然可用，但当前的图生成模型通常过于通用且计算昂贵。在这项工作中，提出了一种新的从头分子设计框架的基础上，不使用原子级递归单元的顺序图生成器的类型。与以前的图形生成模型相比，所提出的方法更适合于分子生成，并且已经扩大到覆盖ChEMBL数据库中的显着更大的分子。结果表明，基于图的模型优于基于SMILES模型在各种指标，特别是在有效输出率。对于药物设计任务的应用，采用了条件图生成模型。该方法灵活性强，适合于多目标的生成。结果表明，这种方法可以有效地应用于解决几个药物设计问题，包括生成含有给定支架的化合物，具有特定药物相似性和合成可及性要求的化合物，以及针对JNK 3和GSK-3β的双重抑制剂。
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3β.
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影响因子：
3.6
作者：
Blaschke T;Olivecrona M;Engkvist O;Bajorath J;Chen H
通讯作者：
Chen H
影响因子：
21.8
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影响因子：
3.5
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Schuffenhauer A