ReviewMedComm2025
Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.
Review in MedComm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy.RSC medicinal chemistry · 2026Review
- Integrated Computational Modeling Reveals a Structurally Plausible Transient Paclitaxel-NK2R Interaction.Bioengineering (Basel, Switzerland) · 2026Article
- Advances and Clinical Translation Potentials of Functional Nanomaterials in Tissue Engineering.Bioengineering (Basel, Switzerland) · 2026Review
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Transformer-accelerated discovery of inhibitors targeting the RpsAJournal of cheminformatics · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Agrimol B inhibits pancreatic ductal adenocarcinoma by induction of lethal mitophagy through decreasing mitochondrial transcription termination factor 3.Precision clinical medicine · 2026Article
- Metalloprotein-Based Nanomedicines: Design Strategies, Functional Mechanisms, and Biomedical Applications.International journal of molecular sciences · 2026Review
- AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases.Frontiers in immunology · 2026Review
- Aligning innovation and security in AI-enabled biotechnology: a framework for designing and funding mutually reinforcing approaches.Frontiers in microbiology · 2026Article
- Latest Advancements in Biotherapeutics.Current pharmaceutical biotechnology · 2026Article
- Artificial Intelligence in the Design and Development of Nanoparticle Drug Delivery Systems: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is revolutionizing biotechnology by transforming the landscape of therapeutic development. Traditional drug discovery faces persistent challenges, including high attrition rates, billion-dollar costs, and timelines exceeding a decade. Recent advances in AI-particularly generative models such as generative adversarial networks, variational autoencoders, and diffusion models-have introduced data-driven, iterative workflows that dramatically accelerate and enhance pharmaceutical R&D. However, a comprehensive synthesis of how AI technologies reshape each key modality of drug discovery remains lacking. This review systematically examines AI-enabled breakthroughs across four major therapeutic platforms: small-molecule drug design, protein binder discovery, antibody engineering, and nanoparticle-based delivery systems. It highlights AI's ability to achieve >75% hit validation in virtual screening, design protein binders with sub-Ångström structural fidelity, enhancing antibody binding affinity to the picomolar range, and optimize nanoparticles to achieve over 85% functionalization efficiency. We further discuss the integration of high-throughput experimentation, closed-loop validation, and AI-guided optimization in expanding the druggable proteome and enabling precision medicine. By consolidating cross-domain advances, this review provides a roadmap for leveraging machine learning to overcome current biopharmaceutical bottlenecks and accelerate next-generation therapeutic innovation.
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.