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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
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Molecular de-novo design through deep reinforcement learning.
Marcus Olivecrona1, Thomas Blaschke2, Ola Engkvist2
1Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, 43183, Mölndal, Sweden. m.olivecrona@gmail.com.
Journal of Cheminformatics
|November 1, 2017
Summary
This study presents a novel generative model for de novo molecular design, capable of creating novel chemical structures with desired properties. The model successfully generates analogues and compounds predicted to be active against biological targets, demonstrating its versatility in drug discovery.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Drug Discovery
Background:
- Generative models are crucial for de novo molecular design.
- Tuning these models for specific properties remains a challenge.
Purpose of the Study:
- To develop a sequence-based generative model for de novo molecular design.
- To enable the generation of molecules with specified desirable properties using augmented episodic likelihood.
Main Methods:
- A sequence-based generative model was employed.
- Augmented episodic likelihood was used for model tuning.
- The model was trained on various tasks, including property-based generation and analogue synthesis.
Main Results:
- The model successfully generated molecules lacking sulfur.
- It produced analogues of Celecoxib for scaffold hopping or library expansion.
- Over 95% of generated compounds targeting dopamine receptor type 2 were predicted active, including novel actives.
Conclusions:
- The developed model offers a powerful approach for property-guided molecular generation.
- This method facilitates diverse applications in drug discovery, from analogue generation to target-specific compound design.
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