SOLD: SELFIES-based Objective-driven Latent Diffusion
This work addresses drug design for pharmaceutical research, but it appears incremental as it builds on existing latent diffusion and SELFIES methods.
The paper tackles the problem of slow and complex de novo drug design by proposing SOLD, a latent diffusion model that generates molecules in a latent space from 1D SELFIES strings conditioned on a target protein, resulting in high-affinity molecules efficiently.
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.