{"ID":2875134,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25198","arxiv_id":"2509.25198","title":"SOLD: SELFIES-based Objective-driven Latent Diffusion","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.25198","url_pdf":"https://arxiv.org/pdf/2509.25198v1","authors":"[\"Elbert Ho\"]","published":"2025-09-03T18:10:23Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}