AI Article Synopsis
- Deep learning models in structural biology often struggle to learn complex rules from limited data due to the difficulty of converging on meaningful solutions.
- MadraX is introduced as a new differentiable force-field that works with deep learning algorithms, allowing for seamless integration.
- Documentation, tutorials, and an installation guide for MadraX can be found at madrax.readthedocs.io.
Article Abstract
Motivation: Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation.
Results: We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion.
Availability And Implementation: MadraX documentation, together with tutorials and installation guide, is available at madrax.readthedocs.io.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11007235 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btae160 | DOI Listing |
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