How AI Has Supercharged Molecular Simulation and Molecular Dynamics
Artificial intelligence has proven invaluable across many applications in countless industries. But, one of the most promising areas in which AI has recently made an impact is structural biology—in particular molecular dynamics (MD), or the simulation of the makeup and movement of molecules and atoms.
Researchers can use powerful AI models to learn more about the structures of molecules, how they’ll behave around other molecules, how atoms and molecules move, and other important data. Here’s how it works.
What is AI for Molecular Simulation?
Molecular simulation (MS) in general isn’t exactly new: The first simulation of a protein molecule took place all the way back in the late 1970s.
AI for molecular simulation, however, is much more novel. It is the use of AI techniques, such as machine learning (ML), to enhance or accelerate the study of molecular systems. This can include areas such as:
Predictive modeling: AI can predict molecular properties and behaviors after being trained on large datasets of already-known molecular structures and properties.
Force fields and potential energy surfaces: While traditional simulations have used classical force fields to model atom interaction, AI can develop new and more accurate force fields to improve accuracy and potentially capture more complex interactions.
Accelerating simulations: Molecular simulations involving complex systems or long timescales require an incredible amount of computing power (and related expense). AI can help optimize sampling techniques and improve computation speeds.
Data analysis: AI’s bread and butter is its ability to analyze massive datasets. Molecular simulations generate huge amounts of data. That’s why AI can help identify patterns, clusters, and other insights on datasets that would take humans years to analyze.
Drug discovery: AI can speed up drug discovery and identify promising drug candidates by predicting how molecules will interact with other biological materials.
Material design: Similarly, AI can help design new materials faster by predicting how changes in molecular structures will affect various properties.
Quantum chemistry: Computationally demanding quantum chemical calculations can benefit greatly from AI. Machine learning models can approximate quantum mechanical calculations, allowing scientists to run larger and more complex simulations.
AI Models for Molecular Simulation in Action
Researchers have already begun applying AI to MS, with encouraging results. Here are some of the most noteworthy initiatives:
Distributional Graphormer (DiG)
Zheng et al. (2024) introduced a novel deep learning framework in this paper on the Distributional Graphormer (DiG) model, designed to predict the equilibrium distribution of molecular systems by using deep neural networks to transform simple distributions into equilibrium distributions. The paper illustrates the model’s performance on a range of MS tasks, including protein conformation sampling, ligand structure sampling, and property-guided structure generation.
Quantum extreme learning
Lo Monaco et al. (2024) show how quantum neural networks can be trained to understand the potential energy surfaces and force fields of molecular systems, using quantum extreme learning machine (QELM). The authors used IBM quantum hardware to run a setup able to study molecules of any dimension, achieving a high level of predictive accuracy on fewer quantum resources than other quantum machine learning methods such as variational quantum eigensolver (VQE).
The technique has several implications for quantum chemistry simulations such as ab initio MD simulations, the authors add.
Generative Chemistry and Accelerated DFT
Microsoft has recently added generative AI capabilities to its Azure Quantum Elements quantum computing platform, in a product offering aimed at the scientific community that the company says will significantly reduce the time to perform MS and MD experiments. The company says it worked with the U.S. Department of Energy’s Pacific Northwest National Laboratory to narrow down a dataset of 32.6 million potential materials for new battery technology to just 18 in four days.
Molecular density functional theory (DFT) is a computational quantum mechanical modelling method used to investigate the structure of many-electron systems.
Peking University
Researchers at Peking University have advocated for the integration of AI in advancing molecular simulation techniques, which could significantly benefit various scientific domains. The authors argue that traditional MD techniques aren’t able to incorporate new advancements in computational technology. AI-aided MS and MD techniques, on the other hand, have modular architectures and Python APIs that allow them to integrate with high-throughput computing devices and facilitate automatic hardware transferability.
Conclusion
While scientists have been able to perform molecular simulations for years, the introduction of quantum computing and AI models have already demonstrated superior results in speeding up and improving the accuracy of such simulations. The technology has practical applications in a range of areas including quantum chemistry, drug discovery, and material design.
Have questions about what AI and ML models can do to accelerate your business and research? Contact the experts at CapeStart, who have years of experience implementing off-the-shelf or proprietary AI models to improve the efficiency and accuracy of medical and scientific research.