Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

De Novo Drug Design using Artificial Intelligence ASYNT-GAN

Version 1 : Received: 8 October 2020 / Approved: 9 October 2020 / Online: 9 October 2020 (11:13:19 CEST)

How to cite: Jacobs, I.; Maragoudakis, M. De Novo Drug Design using Artificial Intelligence ASYNT-GAN. Preprints 2020, 2020100196 (doi: 10.20944/preprints202010.0196.v1). Jacobs, I.; Maragoudakis, M. De Novo Drug Design using Artificial Intelligence ASYNT-GAN. Preprints 2020, 2020100196 (doi: 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.

Subject Areas

drug discovery; artificial intelligence; protein discovery; binding prediction; synthetic molecule generation; synthetic drug

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