Generative Flows on Synthetic Pathway for Drug Design Article Swipe

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Seonghwan Seo , Minsu Kim , Tony S. Shen , Martin Ester , Jinkyoo Park , Sung Soo Ahn , Woo Youn Kim · YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.04542

Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In this paper, we propose RxnFlow, which sequentially assembles molecules using predefined molecular building blocks and chemical reaction templates to constrain the synthetic chemical pathway. We then train on this sequential generating process with the objective of generative flow networks (GFlowNets) to generate both highly rewarded and diverse molecules. To mitigate the large action space of synthetic pathways in GFlowNets, we implement a novel action space subsampling method. This enables RxnFlow to learn generative flows over extensive action spaces comprising combinations of 1.2 million building blocks and 71 reaction templates without significant computational overhead. Additionally, RxnFlow can employ modified or expanded action spaces for generation without retraining, allowing for the introduction of additional objectives or the incorporation of newly discovered building blocks. We experimentally demonstrate that RxnFlow outperforms existing reaction-based and fragment-based models in pocket-specific optimization across various target pockets. Furthermore, RxnFlow achieves state-of-the-art performance on CrossDocked2020 for pocket-conditional generation, with an average Vina score of -8.85 kcal/mol and 34.8% synthesizability.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2410.04542

PDF

https://arxiv.org/pdf/2410.04542

OA Status

green

Cited By

2

Related Works

10

OpenAlex ID

https://openalex.org/W4403322799

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OpenAlex ID

https://openalex.org/W4403322799

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DOI

https://doi.org/10.48550/arxiv.2410.04542

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Title

Generative Flows on Synthetic Pathway for Drug Design

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Type

preprint

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Language

en

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Publication year

2024

Year of publication

Publication date

2024-10-06

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Authors

Seonghwan Seo, Minsu Kim, Tony S. Shen, Martin Ester, Jinkyoo Park, Sung Soo Ahn, Woo Youn Kim

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Landing page

https://arxiv.org/abs/2410.04542

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PDF URL

https://arxiv.org/pdf/2410.04542

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2410.04542

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Concepts

Generative grammar, Drug, Computer science, Generative Design, Artificial intelligence, Business, Biology, Pharmacology, Marketing, Metric (unit)

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Cited by

2

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Citations by year (recent)

2025: 2

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Related works (count)

10

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