ArticleNature communications2023
Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Next-generation Janus kinase inhibitors: Integrating synthetic innovation, structural biology, and computational design for precision drug discovery.Pharmaceutical science advances · 2026Review
- Synthesis of Macrocyclic Structures by Intramolecular Photoclick Reaction.Journal of the American Chemical Society · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review.Journal of cheminformatics · 2026Review
- Synthesis and LCMS Characterization of a 1275-Member Library of 24-Atom Triazine Macrocycles Derived from Quantitative Dimerization Occurring with >99.9% Fidelity.ACS medicinal chemistry letters · 2026Article
- Few-shot molecular property optimizationChemical science · 2026Article
- Macrocycle-DB: a comprehensive database for macrocycle-based drug discovery.Nucleic acids research · 2026Article
- Engineered cyclic peptide targeting ITGA5 disrupts tumor-stroma interaction to overcome desmoplasia and resistance in pancreatic ductal adenocarcinoma.Acta pharmaceutica Sinica. B · 2026Article
- Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.Acta pharmaceutica Sinica. B · 2026Article
- Targeting JAK2 with a multifunctional nanoinhibitor for long-lasting anti-inflammatory effects and bone repair.Journal of nanobiotechnology · 2025Article
- Exploring the macrocyclic chemical space for heuristic drug design with deep learning models.Communications chemistry · 2025Article
- MaskMol: knowledge-guided molecular image pre-training framework for activity cliffs with pixel masking.BMC biology · 2025Article
- Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model.Briefings in bioinformatics · 2025Article
- Unraveling the therapeutic landscape of approved non-peptide macrocycles.Acta pharmaceutica Sinica. B · 2025Review
- Application of Free Energy Perturbation (FEP) Methodology for Predicting the Binding Affinity of Macrocyclic JAK2 Inhibitor Analogues of Pacritinib.ACS medicinal chemistry letters · 2025Article
- Exploring Macrocyclic Chemical Space: Strategies and Technologies for Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2025Review
- FDA-approved drugs featuring macrocycles or medium-sized rings.Archiv der Pharmazie · 2025Review
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- BPI-28592 as a novel second generation inhibitor for NTRK fusion tumors.NPJ precision oncology · 2024Article
- Biological Evaluations and Computer-Aided Approaches of Janus Kinases 2 and 3 Inhibitors for Cancer Treatment: A Review.Pharmaceutics · 2024Review
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.
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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.