ArticleNature communications2024
Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Quantum refinement with electron diffraction and X-ray free-electron laser data: comparative study of ribonucleotide reductase dimetal site.Journal of applied crystallography · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Integrating AI-Based Protein Modeling and Fragment Molecular Orbital Analysis for Structure-Based Drug Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine learning-powered discovery of a novel berberine derivative inducing SCD-dependent ferroptosis in osteosarcoma.Journal of translational medicine · 2025Article
- AQuaRef: machine learning accelerated quantum refinement of protein structures.Nature communications · 2025Article
- Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions.Nature communications · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- Semi-automated modeling of reaction states in time-resolved serial femtosecond crystallography using molecular dynamics sampled conformations.Structural dynamics (Melville, N.Y.) · 2025Article
- Probing the Effect of Protein and Inhibitor Conformational Flexibility on the Reaction of Rocelitinib-Like Covalent Inhibitors of Epidermal Growth Factor Receptor. A Quantum Mechanics/Molecular Mechanics Study.Journal of chemical information and modeling · 2025Article
- Application of Density Functional Theory to Molecular Engineering of Pharmaceutical Formulations.International journal of molecular sciences · 2025Review
- An efficient and flexible approach for local distortion: distortion distribution analysis enabled by fragmentation.Chemical science · 2025Article
- Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein-drug complexes, promote broader QR applications and provide more atomistic insights into drug development.
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