ArticleChemical science2025
Molecule generation for target protein binding with hierarchical consistency diffusion model.
Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- MolPIF: a parameter interpolation flow model for molecule generation.Bioinformatics (Oxford, England) · 2026Article
- Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective generation of molecular structures that bind to target proteins is crucial for lead identification and optimization in drug discovery. Despite advancements in atom- and motif-wise models for 3D molecular generation, current methods often struggle with validity and reliability. To address these issues, we develop the Atom-Motif Consistency Diffusion Model (AMDiff), utilizing a joint-training paradigm for multi-view learning. This model features a hierarchical diffusion architecture that integrates both atom- and motif-views of molecules, allowing for comprehensive exploration of complementary information. By leveraging classifier-free guidance and incorporating topological features as conditional inputs, AMDiff ensures robust molecule generation across diverse targets. Compared to existing approaches, AMDiff exhibits superior validity and novelty in generating molecules tailored to fit various protein pockets. Case studies targeting protein kinases, including Anaplastic Lymphoma Kinase (ALK) and Cyclin-dependent kinase 4 (CDK4), demonstrate the capability in structure-based
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Registered trials
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