Geometry-Complete Diffusion for 3D Molecule Generation and Optimization (2302.04313v6)

Published 8 Feb 2023 in cs.LG, cs.AI, q-bio.BM, q-bio.QM, and stat.ML

Abstract: Denoising diffusion probabilistic models (DDPMs) have pioneered new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein design. Along this latter line of research, methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a DDPM framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively, and generates more novel and unique unconditional 3D molecules for the QM9 dataset compared to previous methods. Importantly, we demonstrate that the geometry-complete denoising process of GCDM learned for 3D molecule generation enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but also that GCDM's geometric features can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Our source code and data are freely available at https://github.com/BioinfoMachineLearning/Bio-Diffusion.

Authors (2)

Alex Morehead (16 papers)
Jianlin Cheng (29 papers)

Citations (10)

View on Semantic Scholar

Summary

Geometry-Complete Diffusion for 3D Molecule Generation and Optimization

In the paper "Geometry-Complete Diffusion for 3D Molecule Generation and Optimization," the authors present an innovative approach to 3D molecule generation utilizing a novel Geometry-Complete Diffusion Model (GCDM). The model leverages denoising diffusion probabilistic models (DDPMs) combined with advances in geometric deep learning to address the limitations of conventional generative models for molecular structures. This is particularly relevant to disciplines that require accurate modeling of molecular geometry and properties, such as computational chemistry and computational biology.

Technical Overview

The proposed GCDM framework incorporates a geometry-complete denoising process that outperforms preceding models in generating realistic and stable molecular structures at larger scales, such as those found in the GEOM-Drugs dataset. The core innovation in GCDM is the integration of geometric-complete neural networks such as GCPNet++, which utilize local reference frames to enhance the expressiveness of 3D graph representations. This is a key differentiator from previous methods that failed to capture essential geometric and physical properties, leading to shortcomings in scalability and realism in generated structures.

Results and Performance

GCDM demonstrates significant improvements over baseline methods across multiple datasets, including the QM9 and GEOM-Drugs datasets, which contain diverse 3D molecular structures. The model shows enhanced ability to generate molecules with high atom and molecule stability, validity, and uniqueness, with results quantitatively substantiated by metrics such as the negative log-likelihood, atom stability, and molecular stability percentages. Standout performance is also observed in conditional generation tasks, where the model can conditionally generate molecules with specific properties more accurately than state-of-the-art methods like GeoLDM.

Further, GCDM exhibits versatility, as shown in experiments where the model optimizes existing 3D molecules for specific properties without retraining, demonstrating its practical applicability in molecular design and optimization tasks.

Implications and Future Directions

The GCDM framework sets a new benchmark in the generative modeling of 3D molecules, addressing prior limitations related to geometric and physical priors in the generative process. Given the importance of structure on the function, especially in tasks like drug discovery and material science, these advancements have substantial practical implications.

Theoretical implications also abound, particularly concerning the capacity of geometry-complete networks to effectively model complex geometric data. Future work could explore the scalability of these models to even larger molecular structures and investigate more efficient sampling techniques to reduce computation times associated with large-scale molecule generation.

In conclusion, the research presented in this paper marks a substantial advancement in the field of 3D molecule generation and optimization, leveraging the powerful combination of DDPMs and geometry-complete networks to address persistent challenges in the domain. GCDM not only pushes the boundaries of current methodologies but also broadens the horizon for future innovations in geometric deep learning applications within molecular sciences.

PDF Markdown

Related Papers

Find Related Papers

GitHub

GitHub - BioinfoMachineLearning/bio-diffusion: A PyTorch hub of denoising diffusion probabilistic models designed to generate novel biological data (209 stars)