ReviewFundamental research2026
Molecule generation for drug design: A graph learning perspective.
Review in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- End-to-End Intelligent Drug Discovery via a Scalable and Explainable Graph-Transformer Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative Architecture.Exploration (Beijing, China) · 2026Article
- AI for biology: Catalyzing interdisciplinary innovation to unravel life's complexity and address biomedical challenges.Fundamental research · 2026Article
- Novel molecule design with POWGAN, a policy-optimized Wasserstein generative adversarial network.Journal of cheminformatics · 2025Article
- Enhancing Unconditional Molecule Generation via Online Knowledge Distillation of Scaffolds.Molecules (Basel, Switzerland) · 2025Article
- A comprehensive review of molecular optimization in artificial intelligence-based drug discovery.Quantitative biology (Beijing, China) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on
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.