  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.
