ReviewMolecules (Basel, Switzerland)2018
Artificial Intelligence in Drug Design.
Review in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
100 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.Journal of biomolecular structure & dynamics · 2022Pooled it
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Image-guided activation of drugs with electromagnetic radiation.Nature chemical biology · 2026Review
- Review
- Spectral Decomposition of Chemical Semantics for Activity Cliffs-Aware Molecular Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.Frontiers in oncology · 2026Review
- Structural and Computational Insights into the Angiotensin II Type 1 Receptor: Advances in Antagonist Design and Implications for Hypertension Therapy (2020-2024).Biomolecules · 2025Review
- Drug Design and Delivery for Intracellular Bacteria: Emerging Paradigms.Drug development research · 2025Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions.Journal of chemical information and modeling · 2025Article
- AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.Scientific reports · 2025Article
- Integrating Graph Convolution and Attention Mechanism for Kinase Inhibition Prediction.Molecules (Basel, Switzerland) · 2025Article
- Neural Network Models for Prediction of Biological Activity using Molecular Dynamics Data: A Case of Photoswitchable Peptides.Molecular informatics · 2025Article
- Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2025Review
- A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Autonomous Nucleic Acid and Protein Nanocomputing Agents Engineered to Operate in Living Cells.ACS nano · 2025Review
- Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.Scientific reports · 2025Article
- Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems.Current topics in medicinal chemistry · 2025Review
- Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development.Current drug discovery technologies · 2025Review
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.
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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.