REINFORCED SEQ2SEQ ADVERSARIAL AUTOENCODER FOR DE NOVO MOLECULAR DESIGN

REINFORCED SEQ2SEQ ADVERSARIAL AUTOENCODER FOR DE NOVO MOLECULAR DESIGN

Subject of Research.The modern models of deep training for generation of target small organic molecules are studied. The studies were carried out on two datasets of 250,000 drug-like molecular compounds from the ZINC database and 23,000 kinase molecular structures collected manually from the open a...

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Bibliographic Details
Main Author: Putin E.O.
Format: Article
Language:English
Published: Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) 2018-10-01
Series:Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki
Subjects:
and also allows for the input conditions for the generation. Main Results. Comparative experiments have shown that the proposed model is better than its closest competitors in experiments with pre- and post-training in terms of generating valid and unique molecular structures. Additional chemical analysis of generated structures demonstrates the best quality of the introduced model in comparison with the other competitor models. Practical Relevance. The proposed model can be used by medical chemists as an intelligent assistant for development of new drugs.
machine learning
deep learning
reinforcement learning
generative adversarial networks
drug design and development
Online Access:https://ntv.ifmo.ru/file/article/18253.pdf

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https://ntv.ifmo.ru/file/article/18253.pdf

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