ArticleJournal of cheminformatics2025
AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities?
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Venom variation and the future of antivenom design: Integrating population venomics, evolutionary toxinology, and precision therapeutics.Toxicon: X · 2026Review
- In Silico Drug Design and Discovery: Big Data for Small Molecule Design-2nd Edition.Biomolecules · 2026Article
- VNFlow: integration of variational autoencoders and normalizing flows for novel molecular design.Journal of cheminformatics · 2025Article
- Overcoming translational barriers in RNA-protein docking: enhancing computational accuracy for targeted drug discovery.Future medicinal chemistry · 2025Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cheminformatics and chemical databases are essential to drug discovery. However, machine learning (ML) and artificial intelligence (AI) methodologies are changing the way in which chemical data is used. How will the use of chemical data change in drug discovery moving forward? How do the new ML methods in molecular property prediction, hit and lead and target identification and structure prediction differ and compare with previous computational methods? Will new ML methodologies improve chemical diversity in ligand design, and offer computational enhancements. There are still many advantages to physics based methods and they offer something lacking in ML/ AI based methods. Additionally, ML training methods often give the best results when experimental assay measurements are fed back into the model. Often modeling and experimental methods are not diametrically opposed but offer the greatest advantage when used complementary.
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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.