{"doi":"10.1111/cts.70149","title":"AI In Action: Redefining Drug Discovery and Development","abstract":"Artificial intelligence (AI) is a field integrating computer science, statistics, and engineering to develop systems capable of performing tasks that typically require human intelligence. AI applications for healthcare span from drug discovery to postmarket safety surveillance and advanced pharmaceutical manufacturing. This perspective provides insights into the application of AI in drug discovery, translational and late-phase development and summarizes the perspectives of the clinical pharmacology community based on survey results, highlighting potential impacts on future research. [Correction added on 10 February 2025, after first online publication: the introductory paragraph was inadvertently published as part of the Acknowledgments section, but is now in the correct location in this version.] The 2024 Nobel Prize in Chemistry was awarded to David Baker, Demis Hassabis, and John Jumper for their groundbreaking work in using AI to predict protein structures and design functional proteins. The development of the AlphaFold model has solved a long-standing challenge in biology by accurately predicting the complex structures of proteins, which are crucial for understanding their function. AlphaFold enhances our ability to design new proteins with specific functions and accelerates drug discovery and development by providing detailed insights into protein behavior and interactions. The recognition of this work underscores the transformative potential of AI in the life sciences and its critical role in future drug research and development (R&D). AI has revolutionized the drug discovery space in recent years, with applications ranging from highly accurate structure predictions of proteins [1], to the design and optimization of both small and large molecules [2]. Several large foundational models have been developed for encoding functional information of proteins in a powerful way to support the drug development pipeline [3, 4]. Figure 1 highlights the areas in the pipeline where AI now plays a significant role and is poised to disrupt traditional experimental techniques. The culmination of AI-driven discovery is de novo design, where the entire preclinical pipeline can be performed in silico, resulting in billions of dollars of R&D cost savings, translating to reduced costs of medications and higher clinical success rates via optimization of safer and more developable molecules showing strong efficacy for well-selected targets. While de novo design is as-yet unproven, the success rate of the 21 AI-developed drugs that have completed Phase I trials as of December 2023 is 80%–90%, significantly higher than ~40% for traditional methods [5]. We continue to see an increase in the number of candidate drugs developed using AI enter clinical stages, and this trend is growing at an exponential rate—from 3 in 2016 to 17 in 2020 and 67 in 2023 [5]. The intersection between high-quality data access across life science modalities like imaging, multi-omics, DMRs, and very large protein repertoires, and recent advancements in the scaling and architecture of large deep learning models has led to an explosion in AI applications for healthcare. While some of this data is publicly available, much of it is proprietary and under the control of large pharmaceutical companies, partly due to regulatory and privacy concerns. Conversely, innovation in AI for drug discovery is being led by academic and industry research laboratories, often resulting in highly funded spin-off ventures like Genentech, Recursion, Absci, and more recently, Evolutionary Scale. Such AI-first life sciences companies have found success in synergistic partnerships with large pharmaceutical companies, thereby gaining access to the large proprietary datasets upon which to apply their AI expertise. Some of these partnerships have led to acquisitions such as the 2009 purchase of Genentech by Roche for approximately $46.8 billion, highlighting the value that AI internalization brings to ","journal":"Clinical and Translational Science","year":2025,"id":512362,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9591,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1081694,"name":"Mark Sale","orcid":"0000-0002-6239-2359","position":1,"is_corresponding":false},{"id":447429,"name":"Liang Zhao","orcid":"0000-0002-0257-9082","position":2,"is_corresponding":false},{"id":1186400,"name":"Zhu Zhou","orcid":"0000-0002-3384-1346","position":3,"is_corresponding":false},{"id":1372514,"name":"Anshul Kanakia","orcid":null,"position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":null,"created_at":"2026-07-19T02:48:06.263458Z","pmid":"39912678","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}