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Deep Learning with Proteins
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arXiv Machine Learning
Jun 11
DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics
arXiv:2606. 11651v1 Announce Type: new Abstract: Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials.
arXiv Machine Learning
Jun 15
Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification
arXiv:2605. 29228v2 Announce Type: replace Abstract: Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural feature(s).
arXiv AI
Aug 6
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
arXiv:2411. 04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive.
arXiv Machine Learning
Sep 4
SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign
SimpleDesign is a single-stage, end-to-end model for joint protein sequence and structure design that eliminates the need for multi-stage training. It combines discrete cross-entropy for sequences with a regression objective for structures, using a Mixture-of-Transformer architecture to handle modality-specific processing while maintaining global self-attention. Trained on over 2 million sequence-structure pairs, SimpleDesign achieves strong performance on co-design and unconditional generation benchmarks.
arXiv Machine Learning
Sep 7
Deep Learning-Driven Peptide Classification in Biological Nanopores
The paper presents a deep learning approach that converts nanopore resistive pulse signals into scaleograms using continuous wavelet transforms, enabling the classification of peptides as an image‑classification problem. On a dataset of 42 peptides, the method achieves an 82% macro‑averaged accuracy, outperforming previous descriptor‑based techniques by 8.6 percentage points. The models also remain accurate after significant weight pruning and 8‑bit quantization, making them suitable for deployment on embedded sensing hardware.
arXiv AI
Sep 15
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
The paper discusses tensorizing neural networks by reshaping dense weight matrices into higher-order tensors and approximating them with low-rank tensor network decompositions. This approach offers promising model compression and introduces bond indices that create new latent spaces, potentially enhancing interpretability. Despite encouraging empirical results, tensorized neural networks remain underused, and the authors call for more research to address practical scaling and adoption challenges.