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        <title>Repurformer: Transformers for Repurposing-Aware Molecule Generation</title>
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        <namePart type="given">Changhun</namePart>
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            <title>Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)</title>
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            <namePart type="given">Carl</namePart>
            <namePart type="family">Edwards</namePart>
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            <namePart type="given">Manling</namePart>
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        <name type="personal">
            <namePart type="given">Heng</namePart>
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    <abstract>Generating as diverse molecules as possible with desired properties is crucial for drug discovery research, which invokes many approaches based on deep generative models today. Despite recent advancements in these models, particularly in variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, and diffusion models, a significant challenge known as the sample bias problem remains. This problem occurs when generated molecules targeting the same protein tend to be structurally similar, reducing the diversity of generation. To address this, we propose leveraging multi-hop relationships among proteins and compounds. Our model, Repurformer, integrates bi-directional pretraining with Fast Fourier Transform (FFT) and low-pass filtering (LPF) to capture complex interactions and generate diverse molecules. A series of experiments on BindingDB dataset confirm that Repurformer successfully creates substitutes for anchor compounds that resemble positive compounds, increasing diversity between the anchor and generated compounds.</abstract>
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%0 Conference Proceedings
%T Repurformer: Transformers for Repurposing-Aware Molecule Generation
%A Lee, Changhun
%A Lee, Gyumin
%Y Edwards, Carl
%Y Wang, Qingyun
%Y Li, Manling
%Y Zhao, Lawrence
%Y Hope, Tom
%Y Ji, Heng
%S Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lee-lee-2024-repurformer
%X Generating as diverse molecules as possible with desired properties is crucial for drug discovery research, which invokes many approaches based on deep generative models today. Despite recent advancements in these models, particularly in variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, and diffusion models, a significant challenge known as the sample bias problem remains. This problem occurs when generated molecules targeting the same protein tend to be structurally similar, reducing the diversity of generation. To address this, we propose leveraging multi-hop relationships among proteins and compounds. Our model, Repurformer, integrates bi-directional pretraining with Fast Fourier Transform (FFT) and low-pass filtering (LPF) to capture complex interactions and generate diverse molecules. A series of experiments on BindingDB dataset confirm that Repurformer successfully creates substitutes for anchor compounds that resemble positive compounds, increasing diversity between the anchor and generated compounds.
%U https://aclanthology.org/2024.langmol-1.14
%P 116-127

Markdown (Informal)

[Repurformer: Transformers for Repurposing-Aware Molecule Generation](https://aclanthology.org/2024.langmol-1.14) (Lee & Lee, LangMol-WS 2024)

• Repurformer: Transformers for Repurposing-Aware Molecule Generation (Lee & Lee, LangMol-WS 2024)

ACL

• Changhun Lee and Gyumin Lee. 2024. Repurformer: Transformers for Repurposing-Aware Molecule Generation. In Proceedings of the 1st Workshop on Language + Molecules (L+M 2024), pages 116–127, Bangkok, Thailand. Association for Computational Linguistics.