ReviewMolecules (Basel, Switzerland)2024
Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.
Review in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- ADMET-XSpec: A Tool for Systematic Cross-Species Data Integration in ADMET Prediction.Chemical research in toxicology · 2026Article
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Administrative Perspectives on Digital Workflow Transformation and Artificial Intelligence Implementation in Dental Clinics.Dentistry journal · 2026Article
- Ginger Bioactives as Multi-Target Therapeutics: Mechanisms, Delivery Innovation, and Human Health Impact.Nutrients · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects.Journal of pharmaceutical analysis · 2025Review
- The Pharmaceutical Industry's Future: How Artificial Intelligence is Transforming Medicine.Advanced pharmaceutical bulletin · 2025Review
- Precision Oncology Through Dialogue: AI-HOPE-RTK-RAS Integrates Clinical and Genomic Insights into RTK-RAS Alterations in Colorectal Cancer.Biomedicines · 2025Article
- Accurate Prediction of Drug Activity by Computational Methods: Importance of Thermal Capacity.Molecules (Basel, Switzerland) · 2025Article
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 2025Review
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Development and experimental validation of a machine learning model for the prediction of new antimalarials.BMC chemistry · 2025Article
- Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Frontiers in pharmacology · 2025Article
- From bench to bedside: targeting ferroptosis and mitochondrial damage in the treatment of diabetic cardiomyopathy.Frontiers in endocrinology · 2025Review
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- A bibliometric analysis of the advance of artificial intelligence in medicine.Frontiers in medicine · 2025Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities.International journal of molecular sciences · 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
5 authors.
Funding
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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.