LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation

TL;DR

This paper introduces LMDM, a latent diffusion model for 3D molecule generation that enhances diversity, preserves geometric features, and improves convergence speed by modeling forces and local constraints in latent space.

Contribution

The paper presents a novel latent molecular diffusion model that captures atomic forces and constraints directly in latent space, reducing computation and improving molecule diversity and geometric fidelity.

Findings

Generated molecules are more diverse and geometrically accurate.

Model converges faster compared to traditional methods.

Sample quality and convergence speed are significantly improved.

Abstract

n this work, we propose a latent molecular diffusion model that can make the generated 3D molecules rich in diversity and maintain rich geometric features. The model captures the information of the forces and local constraints between atoms so that the generated molecules can maintain Euclidean transformation and high level of effectiveness and diversity. We also use the lowerrank manifold advantage of the latent variables of the latent model to fuse the information of the forces between atoms to better maintain the geometric equivariant properties of the molecules. Because there is no need to perform information fusion encoding in stages like traditional encoders and decoders, this reduces the amount of calculation in the back-propagation process. The model keeps the forces and local constraints of particle bonds in the latent variable space, reducing the impact of underfitting on the surface of the network on the large position drift of the particle geometry, so that our model can converge earlier. We introduce a distribution control variable in each backward step to strengthen exploration and improve the diversity of generation. In the experiment, the quality of the samples we generated and the convergence speed of the model have been significantly improved.