DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910514927370240 |
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| author | Fröhlking, Thorben Rizzi, Valerio Aureli, Simone Gervasio, Francesco Luigi |
| author_facet | Fröhlking, Thorben Rizzi, Valerio Aureli, Simone Gervasio, Francesco Luigi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables Fröhlking, Thorben Rizzi, Valerio Aureli, Simone Gervasio, Francesco Luigi Chemical Physics 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. |
| title | DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2407.04376 |