ArticleChemical science2021
Deep generative design with 3D pharmacophoric constraints.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MMP2Mol: a matched molecular pairs-based framework for ligand-based de novo drug design.Briefings in bioinformatics · 2026Article
- AIDrugDesigner: a web server for drug-like molecules generation and optimization.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- ActivityDiff: a diffusion model with positive and negative activity guidance for de novo drug design.Bioinformatics (Oxford, England) · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Diffusion-Based Generative Model With Scaffold-Hopping Strategy Yields Highly Potent Bioactive Molecules.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.Chemical science · 2026Article
- Synthesis and machine learning techniques to enable data-driven investigation of supramolecular host-guest interactions.Chemical science · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Bellerophon: An Automated Tool for PROTAC Decomposition.ACS medicinal chemistry letters · 2026Article
- Integrative Computational Chemistry Approaches in Modern Drug Discovery: Advances in Docking, Pharmacophore Modeling, Molecular Dynamics, and Virtual Screening.Pharmaceutics · 2026Review
- CReM-pharm: de novo 3D pharmacophore-based design with synthetic accessibility awareness.Journal of cheminformatics · 2026Article
- DeepDegradome: A structure-aware deep learning framework for PROTAC and ligand generation against protein targets.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.Computational and structural biotechnology journal · 2026Article
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- Structure-based design of anticancer drugs based on β-elemene: Research foundations and development potential.Journal of pharmaceutical analysis · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Molecule generation for target protein binding with hierarchical consistency diffusion model.Chemical science · 2025Article
- Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products.Natural products and bioprospecting · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding. This work describes a method to incorporate such information and demonstrates the benefit of doing so. We combine an existing graph-based deep generative model, DeLinker, with a convolutional neural network to utilise physically-meaningful 3D representations of molecules and target pharmacophores. We apply our model, DEVELOP, to both linker and R-group design, demonstrating its suitability for both hit-to-lead and lead optimisation. The 3D pharmacophoric information results in improved generation and allows greater control of the design process. In multiple large-scale evaluations, we show that including 3D pharmacophoric constraints results in substantial improvements in the quality of generated molecules. On a challenging test set derived from PDBbind, our model improves the proportion of generated molecules with high 3D similarity to the original molecule by over 300%. In addition, DEVELOP recovers 10× more of the original molecules compared to the baseline DeLinker method. Our approach is general-purpose, readily modifiable to alternate 3D representations, and can be incorporated into other generative frameworks. Code is available at https://github.com/oxpig/DEVELOP.
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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.