Path-like collective variables can be very effective for accurately modeling complex biomolecular processes in molecular dynamics simulations. Recently, we introduced DeepLNE, a machine learning-based path-like CV that provides a progression variable s along the path as a non-linear combination of several descriptors, effectively approximating the reaction coordinate. However, DeepLNE is computationally expensive for realistic systems needing many descriptors and limited in its ability to handle multi-state reactions. Here we present DeepLNE++, which uses a knowledge distillation approach to significantly accelerate the evaluation of DeepLNE, making it feasible to compute free energy landscapes for large and complex biomolecular systems. In addition, DeepLNE++ encodes system-specific knowledge within a supervised multitasking framework, enhancing its versatility and effectiveness.
@article{arxiv.2407.04376,
title = {DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables},
author = {Thorben Fröhlking and Valerio Rizzi and Simone Aureli and Francesco Luigi Gervasio},
journal= {arXiv preprint arXiv:2407.04376},
year = {2024}
}