The Impact of Generative Artificial Intelligence on Entrepreneurship and Innovation in AI-Driven Economies: A Study on Support for AI-Based Startups, Technology Transfer and Innovation Ecosystems

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DOI: 10.1162/jmlr. 21.23.1. https://www.axd.agency/post/the-role-of-ai-in-marketing Artificial intelligence in drug discovery: methods, applications and future perspectives” by Wang et al., published in the journal Expert Opinion on Drug Discovery in 2021. DOI: 10.1080/17460441.2021.18772021 Hall, P., Wang, N., & Montibeller, G. (2019). Explainable Artificial Intelligence . https://www.scirp.org/reference/ReferencesPapers?ReferenceID=2328420 Mia, A. (2017). Adobe Announces Sensei, Its AI System. Digital Art. Retrieved from https://www.digitalartsonline.co.uk/news/creative-software/adobe-announces-sensei-its- ai-system/ https://www.adobe.com/content/dam/cc/hk_en/newsroom/pdf/2023/Adobe_Announces_New_Se nsei_GenAI_Services_to_Reimagine_End-to- End_Marketing_Workflows_HK_EN_20230328.pdf OpenAI: OpenAI’s GPT models exemplify GAI's capacity in generating human-like text and revolutionising natural language processing. [Reference: GPT-1 to GPT-4: Each of OpenAI's GPT Models Explained and Compared (makeuseof.com) Retrieved: December, 2023. Google's DeepMind: Research conducted by DeepMind has resulted in notable breakthroughs in healthcare, particularly with AlphaFold’s advancements in protein folding predictions. [Reference: DeepMind's publications and research.] Conversational AI applications have advanced significantly, as evidenced by recent developments in natural language processing. [Reference: OpenAI's ChatGPT documentation and research.] IBM’s Watson has contributed to cognitive computing and data analysis across various industries. [Reference: IBM's publications and Watson's case studies.] The Netflix recommendation engine demonstrates the application of advanced algorithms in personalised content delivery. [Reference: Netflix's publications on recommendation algorithms.] Generative design methodologies have enabled innovative solutions in architecture and engineering. [Reference: Industry publications on generative design.] The application of advanced computational methods has accelerated research processes and drug development in the pharmaceutical industry. [Reference: Scientific publications on AI in drug discovery.] Jimenez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Exp Opin Drug Disc. 2021;16:949–959. doi: 10.1080/17460441.2021.1909567. - DOI - PubMed AI-powered drug discovery has emerged as a transformative field within pharmaceutical research, revolutionising traditional drug development processes. Several scientific publications have highlighted the pivotal role of Generative Artificial Intelligence in accelerating drug discovery. Here are some key references and areas of research focusing on AI in drug discovery: 'Deep learning for drug discovery and cancer research: a concise review' (PMID: 30858194). This publication provides an overview of deep learning applications in drug discovery and cancer research, emphasising the potential of AI-driven techniques to identify novel drug candidates and optimise therapeutic outcomes. 'Generative Artificial Intelligence in Drug Discovery' (DOI: 10.1016/j.tibtech.2021.07.004). This review discusses the advancements and applications of generative AI in drug discovery, covering topics such as molecular generation, compound optimisation, and de novo drug design. 'AI for drug discovery: What to expect, and how to prepare' (DOI: 10.1126/science.aax4690). This publication outlines the current landscape and future prospects of AI in drug discovery, addressing challenges, opportunities, and the integration of AI technologies to expedite the drug development pipeline. 'Machine learning applications in drug development' (PMID: 33014169) explores the diverse applications of machine learning in drug development, including target identification, compound screening, lead optimisation, and predictive modelling for pharmacokinetics and toxicology. 'Artificial intelligence for clinical trial design' (DOI: 10.1038/s41573-020-0077-7) discusses the utilisation of AI in optimising clinical trial design and execution, focusing on enhancing efficiency, reducing costs, and improving patient outcomes in drug discovery. 'AI-based approaches in early drug discovery: A review' (DOI: 10.1016/j.drudis.2021.07.010) provides a comprehensive review of AI-based methodologies employed in early-stage drug discovery, encompassing virtual screening, molecular modelling, and compound optimisation. 'Deep generative models in chemoinformatics: a review' (DOI: 10.1039/c9sc05986g) Explores the applications of deep generative models in chemoinformatics, elucidating their role in molecular generation, property prediction, and structure-activity relationship analysis. These publications highlight the significant contributions and potential of AI, particularly generative AI, in reshaping the drug discovery landscape. GAI-driven approaches offer innovative solutions for faster, more efficient identification of potential drug candidates, optimisation of molecular structures, and streamlining various stages of the drug development process, ultimately leading to more effective treatments for various diseases. These case studies exemplify GAI's profound influence on diverse sectors, illuminating its role in shaping entrepreneurship and fostering innovation in AI-driven economies.' Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumeé III, H., & Crawford, K. (2019). 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