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Bidirectional Molecule Generation with Recurrent Neural Networks.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-01-06 , DOI: 10.1021/acs.jcim.9b00943
Francesca Grisoni 1 , Michael Moret 1 , Robin Lingwood 1 , Gisbert Schneider 1
Affiliation  

Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL .

中文翻译:

具有递归神经网络的双向分子生成。

递归神经网络(RNN)能够使用化学结构的简化分子输入线输入系统(SMILES)字符串表示来生成从头设计分子。通常,通过从左到右生长SMILES字符串来单向执行基于RNN的结构生成。但是,小分子没有自然的开始或结束,SMILES字符串本质上是分子图的非唯一表示形式。这些特性促使双向结构生成。在此,介绍了基于SMILES的分子设计的双向生成RNN。为此,实现了两个已建立的双向方法,并介绍了一种用于SMILES字符串生成和数据扩充的新方法-通过交替学习进行双向分子设计(BIMODAL)。就(i)新颖性,(ii)支架多样性和(iii)计算机生成分子的化学生物学相关性而言,将这三种双向策略与用于SMILES字符串生成的单向正向RNN方法进行了比较。结果积极地倡导了基于SMILES的分子从头设计的双向策略,在测试条件下,BIMODAL对于大多数标准显示出优于单向正向RNN的结果。方法和预训练模型的代码可以在URL https://github.com/ETHmodlab/BIMODAL中找到。结果积极地倡导了基于SMILES的分子从头设计的双向策略,在测试条件下,BIMODAL对于大多数标准显示出优于单向正向RNN的结果。方法和预训练模型的代码可以在URL https://github.com/ETHmodlab/BIMODAL中找到。结果积极地倡导了基于SMILES的分子从头设计的双向策略,在测试条件下,BIMODAL对于大多数标准显示出优于单向正向RNN的结果。方法和预训练模型的代码可以在URL https://github.com/ETHmodlab/BIMODAL中找到。
更新日期:2020-01-06
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