International Journal of Technology and Applied Science

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Volume 17 Issue 7
July 2026

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Generative AI and Large Language Models in Drug Discovery and Pharmaceutical Sciences: Applications, Limitations and Ethical Considerations

Author(s) Mr. Shouvik Mondal, Mr. Abhijeet Panigrahi
Country India
Abstract The rise of generative artificial intelligence (AI) and large language models (LLMs) marks a revolutionary shift in pharmaceutical science. Transformer-based architectures such as GPT, BERT, Llama, and Claude have shown transformative potential throughout the entire drug discovery and development process, from target identification to clinical trial optimisation.
This review, based on five key peer-reviewed studies from 2024–2025, thoroughly explores the latest advancements in applying large language models (LLMs) to pharmaceutical sciences. The uses of LLMs in drug target identification, molecular design, virtual screening, ADMET prediction, drug repurposing, clinical trials, and analytical chemistry are compiled in this study.
Even though rentosertib (INS018_055) has advanced into Phase 2a clinical trials, there are still several obstacles to overcome, such as hallucinations, poor interpretability, problems with data quality, inadequate domain knowledge integration, and high computing demands. It is also necessary to address ethical issues including algorithmic bias, dual-use dangers, intellectual property, and patient data privacy. Multimodal LLMs, retrieval-augmented generation (RAG), parameter-efficient fine-tuning (PEFT), and responsible clinical implementation are the main areas of future study.
Keywords Large Language Models (LLMs); Generative AI; Drug Discovery; Pharmaceutical Sciences; ADMET; Clinical Trials; AI Ethics; Drug Repurposing
Field Chemistry > Pharmacy
Published In Volume 17, Issue 7, July 2026
Published On 2026-07-13
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Crossref DOI prefix of IJTAS is 10.71097/IJTAS

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