ArticleNature communications2023
A pharmacophore-guided deep learning approach for bioactive molecular generation.
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 51 papers.
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Who cites it
51 citing papers in PubMed.
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- ActivityDiff: a diffusion model with positive and negative activity guidance for de novo drug design.Bioinformatics (Oxford, England) · 2026Article
- Diffusion-Based Generative Model With Scaffold-Hopping Strategy Yields Highly Potent Bioactive Molecules.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.Bioinformatics (Oxford, England) · 2026Article
- Innovative strategies for anti-fibrotic drugs discovery from traditional Chinese medicine.Chinese herbal medicines · 2026Review
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- Leucettamine B and nacryline derivative promote the first steps of endochondral ossification in vitro.Scientific reports · 2026Article
- CReM-pharm: de novo 3D pharmacophore-based design with synthetic accessibility awareness.Journal of cheminformatics · 2026Article
- NumMolFormer: an explicit functional group number-guided framework for structure-based drug design.Bioinformatics (Oxford, England) · 2026Article
- Learned Conformational Space and Pharmacophore Into Molecular Foundational Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design.Interdisciplinary sciences, computational life sciences · 2026Article
- Shared molecular mechanisms in aging and neurodegenerative diseases: from mechanistic insights to therapeutic strategies.Frontiers in human neuroscience · 2026Review
- HIT101308137 and HIT104293658 nominate dual target chemotypes for PTPN1 and PTPN2 with preliminary selectivity in colorectal cancer cells.Frontiers in chemistry · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.
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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.