SOLD: SELFIES-based Objective-driven Latent Diffusion
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909815021764608 |
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| author | Ho, Elbert |
| author_facet | Ho, Elbert |
| contents | Recently, machine learning has made a significant impact on de novo drug design. However, current approaches to creating novel molecules conditioned on a target protein typically rely on generating molecules directly in the 3D conformational space, which are often slow and overly complex. In this work, we propose SOLD (SELFIES-based Objective-driven Latent Diffusion), a novel latent diffusion model that generates molecules in a latent space derived from 1D SELFIES strings and conditioned on a target protein. In the process, we also train an innovative SELFIES transformer and propose a new way to balance losses when training multi-task machine learning models.Our model generates high-affinity molecules for the target protein in a simple and efficient way, while also leaving room for future improvements through the addition of more data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SOLD: SELFIES-based Objective-driven Latent Diffusion Ho, Elbert Machine Learning Recently, machine learning has made a significant impact on de novo drug design. However, current approaches to creating novel molecules conditioned on a target protein typically rely on generating molecules directly in the 3D conformational space, which are often slow and overly complex. In this work, we propose SOLD (SELFIES-based Objective-driven Latent Diffusion), a novel latent diffusion model that generates molecules in a latent space derived from 1D SELFIES strings and conditioned on a target protein. In the process, we also train an innovative SELFIES transformer and propose a new way to balance losses when training multi-task machine learning models.Our model generates high-affinity molecules for the target protein in a simple and efficient way, while also leaving room for future improvements through the addition of more data. |
| title | SOLD: SELFIES-based Objective-driven Latent Diffusion |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.25198 |