Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916384301121536 |
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| author | Arthur-Loui, Heath Mollaysa, Amina Krauthammer, Michael |
| author_facet | Arthur-Loui, Heath Mollaysa, Amina Krauthammer, Michael |
| contents | De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fundamental questions remain unanswered as the field increasingly focuses on more complex generative models and sophisticated molecular representations as an answer to the challenges of drug design. In this paper, we return to the simplest representation of molecules, and investigate overlooked limitations of classical generative approaches, particularly Variational Autoencoders (VAEs) and auto-regressive models. We propose a hybrid model in the form of a novel regularizer that leverages the strengths of both to improve validity, conditional generation, and style transfer of molecular sequences. Additionally, we provide an in depth discussion of overlooked assumptions of these models' behaviour. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation Arthur-Loui, Heath Mollaysa, Amina Krauthammer, Michael Biomolecules Machine Learning De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fundamental questions remain unanswered as the field increasingly focuses on more complex generative models and sophisticated molecular representations as an answer to the challenges of drug design. In this paper, we return to the simplest representation of molecules, and investigate overlooked limitations of classical generative approaches, particularly Variational Autoencoders (VAEs) and auto-regressive models. We propose a hybrid model in the form of a novel regularizer that leverages the strengths of both to improve validity, conditional generation, and style transfer of molecular sequences. Additionally, we provide an in depth discussion of overlooked assumptions of these models' behaviour. |
| title | Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2409.00046 |