Convolutional neural network based on SMILES representation of compounds for detecting chemical motif

• Published in: BMC bioinformatics Vol. 19; no. Suppl 19; pp. 526 - 94
• Main Authors: Hirohara, Maya, Saito, Yutaka, Koda, Yuki, Sato, Kengo, Sakakibara, Yasubumi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 31.12.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analytical chemistry; Artificial intelligence; Artificial neural networks; Binding sites; Bioinformatics; Biomedical and Life Sciences; Chemical compound; Chemical compounds; Chirality; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Convolution; Convolutional neural network; Deoxyribonucleic acid; DNA; Drug discovery; Fingerprints; Functional groups; Genomes; Information processing; Lead compounds; Life Sciences; Machine learning; Methods; Microarrays; Multivariate analysis; Neural networks; Organic chemistry; Performance evaluation; Proteins; Representations; SMILES; Software; Source code; TOX 21 Challenge; Virtual screening; Japan; Virtual screening; SMILES; Convolutional neural network; Chemical compound; TOX 21 Challenge
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2523-5

Background Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. Results We developed another deep learning model for compound classification. In this method, we constructed a distributed representation of compounds based on the SMILES notation, which linearly represents a compound structure, and applied the SMILES-based representation to a convolutional neural network (CNN). The use of SMILES allows us to process all types of compounds while incorporating a broad range of structure information, and representation learning by CNN automatically acquires a low-dimensional representation of input features. In a benchmark experiment using the TOX 21 dataset, our method outperformed conventional fingerprint methods, and performed comparably against the winning model of the TOX 21 Challenge. Multivariate analysis confirmed that the chemical space consisting of the features learned by SMILES-based representation learning adequately expressed a richer feature space that enabled the accurate discrimination of compounds. Using motif detection with the learned filters, not only important known structures (motifs) such as protein-binding sites but also structures of unknown functional groups were detected. Conclusions The source code of our SMILES-based convolutional neural network software in the deep learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/smiles/ , and the dataset used for performance evaluation in this work is available at the same URL.