ReviewJournal of advanced research2025
AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.
Review in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Accelerating the clinical translation of bioengineered anticancer therapeutics.Journal of the National Cancer Center · 2026Article
- MolProphecy: Bridging medicinal chemists' knowledge and molecular pre-trained models via a multi-modal framework.Journal of advanced research · 2026Article
- Computational glycosyltransferases masked deoxynivalenol toxicity and halted FHB spread in wheat grains.Journal of advanced research · 2026Article
- Agentic AI scientists and the rise of virtual laboratories.Annals of medicine and surgery (2012) · 2026Article
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- In Silico Drug Design and Discovery: Big Data for Small Molecule Design-2nd Edition.Biomolecules · 2026Article
- BB-EIT: A Generalized Prediction Model for Protein Adsorption on Polymer Brushes Using Augmented Chemical Embeddings.ACS applied materials & interfaces · 2026Article
- Interpretable Multimodal Molecular Language Model for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- A case study of the application of AI to early stage drug discovery.Scientific reports · 2025Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Generative artificial intelligence, integrative bioinformatics, and single-cell analysis reveal Alzheimer's genetic and immune landscape.Molecular therapy. Nucleic acids · 2025Article
- From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to the recent revolution of artificial intelligence (AI), AI-enabled large language models (LLMs) have flourished and started to be applied in various sectors of science and medicine. Drug discovery and development are time-consuming, complex processes that require high investment. The conventional method of drug discovery is costly and has a high failure rate. AI-enabled LLMs are used in various steps of drug discovery to solve the challenges of time and cost. AIM OF REVIEW: The article aims to provide a comprehensive understanding of AI-enabled LLMs and their use in various steps of drug discovery to ease the challenges. KEY SCIENTIFIC CONCEPTS OF REVIEW: The review provides an overview of the LLMs and their current state-of-the-art application in structure-based drug molecule design and de novo drug design. The different applications of AI-enabled LLMshave been illustrated, such as drug target identification, validation, interaction, and ADME/ADMET. Several domain-specific models of LLMs are developed in this direction and applied in drug discovery and development to speed up the process. We discussed all these domain-specific models of LLMs and their applications in this field. Finally, we illustrated the challenges and future perspectives on the applications of AI-enabled LLMs in drug discovery and development.
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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.