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Found 5 papers, 0 papers with code
Autoregressive fragment-based diffusion for pocket-aware ligand design
In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures.
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules
In this contribution, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale molecular dynamics trajectories.
Fine-Tuning Data Structures for Analytical Query Processing
Furthermore, we show that the performance of the code generated by our framework either outperforms or is on par with the state-of-the-art analytical query engines and a recent in-database machine learning framework.
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders
We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding.
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer
The proposed method has been devoted to both lightweight image classification and encoder-decoder architectures to boost the performance of small and compact models without incurring extra computational overhead at the inference process.
