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A comparison of probabilistic generative frameworks for molecular simulations.

Richard John¹, Lukas Herron^2,3, Pratyush Tiwary^3,4

¹Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.

The Journal of Chemical Physics

|March 21, 2025

Summary

Generative AI models for molecular science show varied performance. Neural spline flows excel in low-dimensional data, conditional flow matching in high-dimensional data, and denoising diffusion probabilistic models in complex low-dimensional data.

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Area of Science:

Molecular Science
Computational Chemistry
Artificial Intelligence

Background:

Generative artificial intelligence (AI) is increasingly utilized in molecular science.
A lack of numerical experiments benchmarks the performance of probabilistic generative models on molecular data.
Understanding model performance is crucial for selecting appropriate tools for molecular tasks.

Purpose of the Study:

To introduce and categorize probabilistic generative models for molecular science.
To benchmark the performance of representative flow-based and diffusion models.
To guide the selection of generative models for diverse molecular applications.

Main Methods:

Categorization of generative models into flow-based and diffusion models.
Selection and implementation of three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models.
Evaluation of model accuracy, computational cost, and generation speed using tunable datasets, including a Gaussian mixture model and peptide dihedral angle distributions.

Main Results:

No single generative model framework is universally superior across all molecular data types and complexities.
Neural spline flows demonstrate superior performance in capturing mode asymmetry in low-dimensional data.
Conditional flow matching excels in high-dimensional, low-complexity data scenarios.
Denoising diffusion probabilistic models show the best performance for low-dimensional, high-complexity data.

Conclusions:

The choice of generative model in molecular science depends heavily on data characteristics such as dimensionality, complexity, and modal asymmetry.
The study provides a valuable taxonomy and empirical evidence to aid researchers in selecting appropriate probabilistic generative models for their specific molecular tasks.
Further research can expand this benchmarking to a wider array of generative models and molecular datasets.