ReviewPharmaceutics2025
Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.
Review in Pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Flavonoids fromJournal of enzyme inhibition and medicinal chemistry · 2026Article
- Confidence-Gated Triage: Coupling Drug-Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Integrated Computational Modeling Reveals a Structurally Plausible Transient Paclitaxel-NK2R Interaction.Bioengineering (Basel, Switzerland) · 2026Article
- Neurons Die Not by One Hit, but by Signaling Convergence.Molecular neurobiology · 2026Review
- Phytochemicals in Alzheimer's Disease Prevention and Management: Molecular Mechanisms, Therapeutic Potential, Translational Challenges, and Emerging Research Directions.International journal of molecular sciences · 2026Review
- MERS-Mpro Predictor: A Machine Learning-Based Tool for Rapid Screening of Potential MERS-CoV Main Protease Inhibitors.International journal of molecular sciences · 2026Article
- A clinical decision framework for redox-adapted, EMT-high cancers: From ferroptosis resistance to precision therapeutic stratification.Redox biology · 2026Review
- Generative Artificial Intelligence Transitions Pharmaceutical Development from Empirical Screening to Predictive Molecular Design and Clinical Trial Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Mechanistic insights into the anti-inflammatory effects of isocoronarin D associated with reduced PKCδ and PI3K/Akt signalling in LPS-activated macrophages.Inflammopharmacology · 2026Article
- Review
- Hybrid Dual-Context Prompted Cross-Attention Framework with Language Model Guidance for Multi-Label Prediction of Human Off-Target Ligand-Protein Interactions.International journal of molecular sciences · 2026Article
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Molecular docking in histological biomarker discovery and disease modeling: techniques, validation, and translational perspectives.Journal of molecular histology · 2026Review
- Shaping the future one slice at a time: How 3D organotypic tumour slice models are driving drug discovery in ovarian cancer.Translational oncology · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- From synapse to system: mechanistic pathways of neural signaling dysfunction in psychiatric disorders.Frontiers in cell and developmental biology · 2026Review
- Phytochemical small molecules fromAmerican journal of translational research · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Training the next-generation of biomedical scientists through artificial intelligence-driven education and research in pharmacology and pharmaceutical sciences.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
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
Abstract
Multi-target drug discovery has become an essential strategy for treating complex diseases involving multiple molecular pathways. Traditional single-target approaches often fall short in addressing the multifactorial nature of conditions such as cancer and neurodegenerative disorders. With the rise in large-scale biological data and algorithmic advances, machine learning (ML) has emerged as a powerful tool to accelerate and optimize multi-target drug development. This review presents a comprehensive overview of ML techniques, including advanced deep learning (DL) approaches like attention-based models, and highlights their application in multi-target prediction, from traditional supervised learning to modern graph-based and multi-task learning frameworks. We highlight real-world applications in oncology, central nervous system disorders, and drug repurposing, showcasing the translational potential of ML in systems pharmacology. Major challenges are discussed, such as data sparsity, lack of interpretability, limited generalizability, and integration into experimental workflows. We also address ethical and regulatory considerations surrounding model transparency, fairness, and reproducibility. Looking forward, we explore promising directions such as generative modeling, federated learning, and patient-specific therapy design. Together, these advances point toward a future of precision polypharmacology driven by biologically informed and interpretable ML models. This review aims to provide researchers and practitioners with a roadmap for leveraging ML in the development of safer and more effective multi-target therapeutics.
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.