Artificial Intelligence and Drug Discovery: Exploring the effects on competencies, resources, and management in pharmaceutical companies

Artificial intelligence (AI) is one of the most relevant and controversial technologies of the 21st century. AI and its subcategories, machine learning (ML) and deep learning (DL), are wide-ranging tools supporting humans during decisional processes. The widespread use of computational methods is a phenomenon observed across almost every sector, from the most traditional to digital and the pharmaceutical industry. For the latter, this has been particularly the case since the Covid-19 crisis. The critical activity of each pharma company is called drug discovery and development (DDD): it is a process that, according to literature, takes an average of 10,5 years (Thomas et al., 2021, p. 24), costing billions of dollars, and with a significant risk of failure. All these factors are leading to investments to boost the potential of AI. The expected outcomes include reduced time to market (TTM), improved quality, automation of processes, and the potential to find treatments for previously untreatable diseases. While there is no doubt that AI can revolutionize the industry, the technology is relatively recent and continuously evolving. The length of the DDD process means that the results are not immediately visible, and the costs of applying computational methods can be high at the beginning, with no important revenue shortly. Despite these concerns, companies are gradually switching to computational pharmaceutics, integrating ML and DL algorithms into their research and development (R&D) operations. These firms have recognized AI's potential. Even if there are challenges to be faced in technology and business transformation, AI can improve the Drug Discovery process, leading to significant benefits for patients and also for the pharmaceutical industry. The aim of this thesis is to explore the impact of artificial intelligence on the pharmaceutical industry, with a particular focus on evolving competencies, capabilities, and resources. Through a comprehensive state-of-the-art review, this study seeks to answer the question: "How is AI reshaping drug discovery management?" To achieve this, a worldwide patent analysis was conducted to investigate the current use of AI in pharmaceutical companies and its increasing adoption trend in the industry. Additionally, a series of qualitative interviews with experts in the field provided insights and feedback on the various ways in which pharma companies are implementing AI, including results achieved, organizational structure changes, problems faced, and potential competitive advantages. The research conducted in this thesis contributes to the pharmaceutical industry in several ways. Firstly, it analyses the current status of AI implementation in the Drug Discovery process, which helps identify the gaps and areas of improvement in the sector. Secondly, it provides practical recommendations for firms seeking to adopt AI technologies, including strategies for building the necessary technical and organizational capabilities. Finally, the research also presents suggestions for managing the integration of AI into the existing DDD processes, which will help companies to ensure a smooth and efficient transition to the new technology.