Aug 2026· Journal of Computer-Aided Molecular Design· Vol 40· 0 citations· 37 references
Medicine
TL;DR
The autoencoder framework encodes protein sequence information related to domains, families, and patterns—into a lengthy, sparse binary vector that outperforms other neural network models, including convolutional neural networks, recurrent neural networks, long short-term memory networks, and bidirectional long short-term memory networks.
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
Guodong Min, Huan Peng· Methods in molecular biology· 0 citations
A neural network-based pipeline that integrates amino acid sequences with structural features is developed and provides a modular prototype for follow-up, more extensive protein modeling, including larger proteins and sequence of variable sizes.
Carl David Jasper Causin, M. Fyta· APL Machine Learning· 0 citations
The novel combination of LZ complexity–based negative sample selection, CT feature representation, and GA-optimized CNN–LSTM architecture provides a robust and biologically informed framework for PPI prediction.
A Gene Bio-BERT based framework is proposed for automated gene function prediction utilizing deep learning methods in biomedical data analysis and attains an exceptional accuracy of 94.5% and F1 score of 0.87.
A long-context protein language model is introduced, trained on entire viral genomes, leveraging a biologically informed sparse attention mechanism in which inter-protein links are inferred computationally and used as sparsity priors.
T. Dejean, Barbra D. Ferrell, Zachary D. Schreiber et al.· GigaScience· 0 citations
The utility of HA sites for suggesting candidate binding sites and the biological interpretability of PLM representations is explored, demonstrating the biological interpretability of PLM representations and offers a valuable method to prioritize functionally relevant protein residues for targeted biomedical research.
Sophia J. Pribus, Russ B. Altman, Gowri Nayar· bioRxiv· 0 citations
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