ArticleBiomolecules2023
Protein Design Using Physics Informed Neural Networks.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025Article
- Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.Molecular pharmaceutics · 2025Review
- The role of bioinformatics algorithms in modern biopharmaceutical design: Progress, challenges, and future perspectives.BioImpacts : BI · 2025Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-II.Biomolecules · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.