SOLD: SELFIES-based Objective-driven Latent Diffusion

• URL: http://arxiv.org/abs/2509.25198v1
• Date: Wed, 03 Sep 2025 18:10:23 GMT
• Title: SOLD: SELFIES-based Objective-driven Latent Diffusion
• Authors: Elbert Ho,
• Abstract summary: We propose a novel latent diffusion model that generates molecules in a latent space derived from 1D SELFIES strings and conditioned on a target protein.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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