Application of Generative Autoencoder in De Novo Molecular Design. Issue 1 (13th December 2017)

• Record Type: Journal Article
• Authors: Blaschke, Thomas; Olivecrona, Marcus; Engkvist, Ola; Bajorath, Jürgen; Chen, Hongming
• Is Part Of: Molecular informatics. Volume 37:Issue 1/2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-12-13
• Subjects: Autoencoder -- chemoinformatics -- de novo molecular design -- deep learning -- inverse QSAR; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201700123
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified. Abstract :