GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (2203.02923v1)

Abstract: Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspired by the diffusion process in classical non-equilibrium thermodynamics where heated particles will diffuse from original states to a noise distribution, in this paper, we propose a novel generative model named GeoDiff for molecular conformation prediction. GeoDiff treats each atom as a particle and learns to directly reverse the diffusion process (i.e., transforming from a noise distribution to stable conformations) as a Markov chain. Modeling such a generation process is however very challenging as the likelihood of conformations should be roto-translational invariant. We theoretically show that Markov chains evolving with equivariant Markov kernels can induce an invariant distribution by design, and further propose building blocks for the Markov kernels to preserve the desirable equivariance property. The whole framework can be efficiently trained in an end-to-end fashion by optimizing a weighted variational lower bound to the (conditional) likelihood. Experiments on multiple benchmarks show that GeoDiff is superior or comparable to existing state-of-the-art approaches, especially on large molecules.

• The paper introduces a geometric diffusion model that treats molecular conformation generation as a Markov process using equivariant kernels.
• The methodology leverages invariant formulations for rotational and translational symmetries to ensure realistic molecular structures.
• Experimental results on datasets like GEOM-QM9 validate GeoDiff's competitive performance, underscoring its potential in drug discovery.

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation

GeoDiff introduces a novel approach in the domain of molecular conformation prediction by utilizing a geometric diffusion model. This model is tailored for generating 3D conformations of molecules based on their molecular graphs, addressing a key problem in cheminformatics and drug discovery.

Theoretical Foundation

The central premise of GeoDiff is inspired by diffusion processes observed in non-equilibrium thermodynamics. In these processes, particles naturally diffuse from stable states to a noise distribution. GeoDiff leverages this concept by implementing a reverse diffusion process modeled as a Markov chain. The innovation lies in the handling of conformation likelihood, ensuring it is invariant to rotational and translational transformations, a pivotal consideration given the natural symmetries of molecular structures.

Methodology

GeoDiff models the conformation generation using denoising diffusion models integrated with geometric representations:

1. Parameterization as a Markov Chain: The model treats the generation of molecular conformations as a Markov process with each atom represented as a particle. The transition dynamics are characterized by equivariant Markov kernels.
2. Equivariance and Invariance: A significant theoretical contribution is showing how a Markov process using these kernels can induce an invariant distribution. This is crucial for ensuring the generated conformations respect natural symmetries.
3. Training: The model optimizes a weighted variational lower bound of the conditional likelihood, which can be trained efficiently in an end-to-end manner.

Experimental Results

GeoDiff was benchmarked against several datasets, including GEOM-QM9 and GEOM-Drugs, demonstrating strong performance. It showed superiority or comparability to state-of-the-art methods, particularly with large and complex molecular structures. The experiments indicated that the model effectively generates diverse and accurate molecular conformations.

Implications and Future Developments

GeoDiff lays foundational work for leveraging diffusion processes in molecular conformation generation, bringing forth both practical and theoretical advancements. The model's consideration of translational and rotational symmetries offers a refined approach which could enhance computational efficiency and accuracy in drug discovery pipelines.

Future developments could include extending GeoDiff's capabilities to more complex molecular systems, including proteins. Moreover, integrating additional recent advancements in diffusion models or optimizing the model's computational aspects could make GeoDiff applicable in broader contexts within AI-driven molecular design.

In conclusion, GeoDiff represents a significant step in molecular conformation generation, offering robust theoretical foundations and proving practical efficacy in preliminary benchmarks, setting the stage for further exploration and application in AI-driven chemistry and materials science.