ArticleNature communications2024
A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets.
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 80 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
80 citing papers in PubMed.
- Controllable molecular generation with fine-tuned flow-matching model.Communications chemistry · 2026Article
- Diffusion-Based Generative Model With Scaffold-Hopping Strategy Yields Highly Potent Bioactive Molecules.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.Journal of cheminformatics · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- Revisiting Target-Aware de novo Molecular Generation with TarPass: Between Rational Design and Texas Sharpshooter.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Impact of molecular multimodality on neural network models for prediction tasks related to drug discovery.Nature communications · 2026Article
- Discovering COJournal of chemical information and modeling · 2026Article
- Assessing the factors influencing the quality of pocket-conditioned 3D generative models.Journal of cheminformatics · 2026Article
- FlowMol3: flow matching for 3DDigital discovery · 2026Article
- Discovery of a triple-site inhibitor targeting bacterial methionyl-tRNA synthetase through combined drug repurposing screening and generative AI-assisted optimization.Nucleic acids research · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Steering semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning.Science advances · 2026Article
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- A diffusion-based framework for designing molecules in flexible protein pockets.Science advances · 2026Article
- Generative AI in structure-based drug discovery.BMC biology · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Interaction-constrained 3D molecular generation using a diffusion model enables structure-based pharmacophore modeling for drug design.npj drug discovery · 2026Article
- Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design.Interdisciplinary sciences, computational life sciences · 2026Article
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (M
Indexed as
Identifiers
What OpenQuestion holds
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.