ReviewJournal of chemical information and modeling2025
Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.
Review in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design.Journal of cheminformatics · 2026Article
- Exploiting the T790M Gatekeeper: A Theoretical Blueprint for Non-Covalent Inhibition of inPharmaceutics · 2026Article
- Revisiting Target-Aware de novo Molecular Generation with TarPass: Between Rational Design and Texas Sharpshooter.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Discovering COJournal of chemical information and modeling · 2026Article
- Reproducibility, validation, and failure modes across classical and AI-driven molecular docking.Journal of computer-aided molecular design · 2026Review
- Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research.The Journal of organic chemistry · 2026Review
- Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision.Pharmacopsychiatry · 2026Review
- Article
- Designing novel linagliptin analogs through combined generative artificial intelligence, molecular docking, molecular dynamics simulation, and binding energy estimation for targeting fibroblast activation protein.Frontiers in chemistry · 2026Article
- Porphyrin pathways as targets for combating antimicrobial resistance: virulence and therapeutic opportunities.Frontiers in cellular and infection microbiology · 2026Review
- Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.Frontiers in oncology · 2026Review
- Engineering Nanocarriers for Dopamine Stabilization and Targeted Brain Delivery: Mechanisms, Approaches and Translational Challenges.International journal of nanomedicine · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery, focusing on the intricate tasks related to small molecule generation, evaluation, and prioritization. Central to this process is navigating conflicting information from diverse sources─balancing chemical diversity, synthesizability, and bioactivity. We discuss the current state of generative methods, their optimization, and the critical need for robust evaluation protocols. By mapping this evolving landscape, we outline key building blocks, inherent dilemmas, and future directions in the journey to fully harness generative deep learning in the "chemical odyssey" of drug design.
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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.