ReviewPharmaceutics2025
Explainable Artificial Intelligence: A Perspective on Drug Discovery.
Review in Pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Breaking through the radiation dilemma: development and clinical translation of anti-radiation drugs.Pharmaceutical science advances · 2026Review
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Confidence-Gated Triage: Coupling Drug-Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking.Pharmaceuticals (Basel, Switzerland) · 2026Article
- An Explainable Machine Learning-Based QSAR Framework for Predicting Thrombin Inhibitory Activity.Pharmaceuticals (Basel, Switzerland) · 2026Article
- The innovation disconnect: Can artificial intelligence finally align academic discovery with industry needs?Indian journal of pharmacology · 2026Article
- Explainable Multi-Isoform QSAR, PubChem Concordance, and Applicability-Domain-Guided Prioritization of Selective Human Carbonic Anhydrase I, II, IX, and XII Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Harnessing human tumor organoids for cancer modeling and precision therapy.Protein & cell · 2026Review
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular Fingerprints.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Ginger Bioactives as Multi-Target Therapeutics: Mechanisms, Delivery Innovation, and Human Health Impact.Nutrients · 2026Review
- AI-Driven Plant-Derived Anti-Infectives: Integrating Traditional Wisdom into Precision Medicine Against AMR.Life (Basel, Switzerland) · 2026Review
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- AI-driven drug reposition for pathogens: a new paradigm in pandemic preparedness.Frontiers in chemistry · 2026Review
- A new paradigm for retroperitoneal leiomyosarcoma: integrating transcriptomic subtyping and surgical risk stratification for AI-guided drug repurposing.Oncology reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The convergence of artificial intelligence (AI) and drug discovery is accelerating the pace of therapeutic target identification, refining of drug candidates, and streamlining processes from laboratory research to clinical applications. Despite these promising advances, the inherent opacity of AI-driven models, especially deep-learning (DL) models, poses a significant "black-box" problem, limiting interpretability and acceptance within the pharmaceutical researchers. Explainable artificial intelligence (XAI) has emerged as a crucial solution for enhancing transparency, trust, and reliability by clarifying the decision-making mechanisms that underpin AI predictions. This review systematically investigates the principles and methodologies underpinning XAI, highlighting various XAI tools, models, and frameworks explicitly designed for drug-discovery tasks. XAI applications in healthcare are explored with an in-depth discussion on the potential role in accelerating the drug-discovery processes, such as molecular modeling, therapeutic target identification, Absorption, Distribution, Metabolism, and Excretion (ADME) prediction, clinical trial design, personalized medicine, and molecular property prediction. Furthermore, this article critically examines how XAI approaches effectively address the black-box nature of AI models, bridging the gap between computational predictions and practical pharmaceutical applications. Finally, we discuss the challenges in deploying XAI methodologies, focusing on critical research directions to improve transparency and interpretability in AI-driven drug discovery. This review emphasizes the importance of researchers staying current on evolving XAI technologies to realize their transformative potential in fully improving the efficiency, reliability, and clinical impact of drug-discovery pipelines.
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.