ReviewPharmaceuticals (Basel, Switzerland)2025
Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes.
Review in Pharmaceuticals (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.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
- Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges.International journal of molecular sciences · 2026Review
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- Article
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Automated Processes and Artificial Intelligence in Generating Candidates for Oncology Drug Repurposing: Three-Year Scoping Review of Data.Pharmacy (Basel, Switzerland) · 2026Review
- The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery.Briefings in bioinformatics · 2026Review
- Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Review
- A strategic discovery roadmap towards high-quality leads and drug development candidates for kinetoplastid diseases. Part 1: setting the stage.The Journal of antimicrobial chemotherapy · 2026Review
- AI-Driven Plant-Derived Anti-Infectives: Integrating Traditional Wisdom into Precision Medicine Against AMR.Life (Basel, Switzerland) · 2026Review
- Accelerating GPCR drug discovery through computation and experiment integrated with direct detection of ligand binding events.npj drug discovery · 2026Review
- Machine Learning (ML) and Molecular Dynamics-Driven Optimization of VEGFR2 Ligands against Hepatocellular Carcinoma.Oncology research · 2026Article
- HYG-mol: An Interpretable Multimodal Hypergraph Framework for Molecular Property Prediction.Computational and structural biotechnology journal · 2026Article
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is emerging as a valuable complementary tool in small-molecule drug discovery, augmenting traditional methodologies rather than replacing them. This review examines the evolution of AI from early rule-based systems to advanced deep learning, generative models, diffusion models, and autonomous agentic AI systems, highlighting their applications in target identification, hit discovery, lead optimization, and safety prediction. We present both successes and failures to provide a balanced perspective. Notable achievements include baricitinib (BenevolentAI/Eli Lilly, an existing drug repurposed through AI-assisted analysis for COVID-19 and rheumatoid arthritis), halicin (MIT, preclinical antibiotic), DSP-1181 (Exscientia, discontinued after Phase I), and ISM001-055/rentosertib (Insilico Medicine, positive Phase IIa results). However, several AI-assisted compounds have also faced challenges in clinical development. DSP-1181 was discontinued after Phase I, despite a favorable safety profile, highlighting that the acceleration of discovery timelines by AI does not guarantee clinical success. Despite progress, challenges such as data quality, model interpretability, regulatory hurdles, and ethical concerns persist. We provide practical insights for integrating AI into drug discovery workflows, emphasizing hybrid human-AI approaches and the emergence of agentic AI systems that can autonomously navigate discovery pipelines. A critical evaluation of current limitations and future opportunities reveals that while AI offers significant potential as a complementary technology, realistic expectations and careful implementation are crucial for delivering innovative therapeutics.
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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.