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This research focuses on developing a framework that enhances the sampling of molecular pathways using machine learning.

Data-driven enhanced sampling of mechanistic pathways

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Demonstrates a data-driven approach to molecular mechanisms in chemical reactions, highlighting the role of neural networks.

This research focuses on developing a framework that enhances the sampling of molecular pathways using machine learning.
Utilized a deep multitask learning algorithm
Combined deep neural networks with well-tempered metadynamics
Iteratively learned minimum free energy pathways between reactants and products.
Improved pathway exploration with a simpler protocol
Eliminated the need for intermediate structure identification along guess paths
Showed applications in chemical reactions, protein folding, and ligand–receptor binding.

Elangovan et al. (2025) studied this question.

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