Version 1
: Received: 8 October 2020 / Approved: 9 October 2020 / Online: 9 October 2020 (11:13:19 CEST)
Version 2
: Received: 19 November 2020 / Approved: 20 November 2020 / Online: 20 November 2020 (11:30:03 CET)
How to cite:
Jacobs, I.; Maragoudakis, M. De Novo Drug Design using Artificial Intelligence ASYNT-GAN. Preprints2020, 2020100196. https://doi.org/10.20944/preprints202010.0196.v1
Jacobs, I.; Maragoudakis, M. De Novo Drug Design using Artificial Intelligence ASYNT-GAN. Preprints 2020, 2020100196. https://doi.org/10.20944/preprints202010.0196.v1
Jacobs, I.; Maragoudakis, M. De Novo Drug Design using Artificial Intelligence ASYNT-GAN. Preprints2020, 2020100196. https://doi.org/10.20944/preprints202010.0196.v1
APA Style
Jacobs, I., & Maragoudakis, M. (2020). De Novo Drug Design using Artificial Intelligence ASYNT-GAN. Preprints. https://doi.org/10.20944/preprints202010.0196.v1
Chicago/Turabian Style
Jacobs, I. and Manolis Maragoudakis. 2020 "De Novo Drug Design using Artificial Intelligence ASYNT-GAN" Preprints. https://doi.org/10.20944/preprints202010.0196.v1
Abstract
In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.
Keywords
drug discovery; artificial intelligence; protein discovery; binding prediction; synthetic molecule generation; synthetic drug
Subject
Medicine and Pharmacology, Pharmacology and Toxicology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
