ReviewMolecular diversity2021
Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.
Review in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Computational applications in secondary metabolite discovery (CAiSMD) 2026: focus on secondary metabolites towards drug discovery.Journal of cheminformatics · 2026Article
- Accelerated drug development using a digital formulator and a self-driving tableting data factory.Nature communications · 2026Article
- Reclaiming the Microbial Battlefield: Adjuvant Strategies to Overcome Antibiotic Resistance.Microorganisms · 2026Review
- BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs.Journal of chemical information and modeling · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- A Multiscale Computational Study for the Identification of Novel Inhibitors Targeting Tau-Tubulin Kinase 1 (TTBK1) in Alzheimer's Disease.Current computer-aided drug design · 2026Article
- Bioisosteric and Virtual Screening Approach to Identify Natural Inhibitors of Chikunguya alphavirus nsP3.Cell biochemistry and biophysics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Physically Transient Gelatin-Based Memristors of Buildable Logic Gates.Gels (Basel, Switzerland) · 2025Article
- Toward Interoperable Digital Medication Records on Fast Healthcare Interoperability Resources: Development and Technical Validation of a Minimal Core Dataset.JMIR medical informatics · 2025Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Artificial intelligence in drug development: reshaping the therapeutic landscape.Therapeutic advances in drug safety · 2025Review
- AI-Based Drug Discovery and Design for Different Genetic Designs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches.Computational and structural biotechnology journal · 2024Review
- Unraveling Cordia myxa's anti-malarial potential: integrative insights from network pharmacology, molecular modeling, and machine learning.BMC infectious diseases · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease.International journal of molecular sciences · 2023Review
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
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.
Indexed as
Identifiers
34110579What 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.