AI in Drug Discovery market in terms of revenue was estimated to be worth $0.6 billion in 2022 and is poised to reach $4.0 billion by 2027, growing at a CAGR of 45.7% from 2022 to 2027 according to a latest report published by MarketsandMarkets™. The growing need to curb the drug discovery cost & reduce the overall time taken in this process, the rising adoption of cloud-based applications and services, and the impending patent expiry of blockbuster drugs are some of the key factors driving the growth of this market.

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Notable AI in Drug Discovery mergers and acquisitions for 2022 – 2022 include:

• In March 2021, Iktos (France) collaborated with Pfizer (US) to apply Iktos’ AI-driven de novo design software to selected small-molecule programs of Pfizer
• In October 2020, Genesis Therapeutics (US) partnered with Genentech (US) for a multi-target drug development deal using Genesis’ graph machine learning capabilities to identify drug candidates for a range of disorders.
• In February 2021, Exscientia (UK) and the University of Oxford collaborated to develop treatments for Alzheimer’s disease

Hypothetic challenges of AI in Drug Discovery market in near future:

• Data Quality and Availability: AI relies heavily on high-quality, diverse, and comprehensive data for training and model development. However, ensuring the availability of reliable and well-curated data sets can be a challenge, especially in the drug discovery field where data privacy, intellectual property, and regulatory considerations are significant. Obtaining access to large and representative data sets that encompass diverse patient populations and disease types may be a hurdle for AI-driven drug discovery efforts.
• Interpretability and Explainability: AI algorithms, particularly deep learning models, often operate as “black boxes,” making it challenging to interpret the reasoning behind their predictions or recommendations. In drug discovery, where decision-making needs to be transparent and explainable, the lack of interpretability may raise concerns among regulators, clinicians, and patients. Addressing the interpretability challenge of AI models is crucial to build trust and acceptance in the field.
• Validation and Regulatory Compliance: Validating AI-driven models and ensuring compliance with regulatory standards present challenges in the drug discovery industry. Regulators typically require a high level of evidence and validation to ensure the safety and efficacy of new drugs. AI models must meet rigorous standards and demonstrate robust performance on diverse datasets to gain regulatory approval. Developing a regulatory framework that adequately addresses the unique considerations of AI in drug discovery is essential to enable its wider adoption.
• Ethical Considerations: The use of AI in drug discovery raises ethical concerns, such as the potential bias in algorithmic decision-making, privacy and security of patient data, and the impact on employment in the pharmaceutical industry. Ensuring fairness, transparency, and accountability in AI systems is essential to mitigate these ethical challenges and ensure that AI-driven drug discovery benefits all stakeholders.
• Human-AI Collaboration: While AI can enhance and accelerate drug discovery processes, it is not a substitute for human expertise. Effective collaboration between AI systems and human researchers is crucial to leverage the strengths of both. Integrating AI into existing workflows, addressing the challenges of data integration, and fostering a culture that encourages collaboration between AI experts and domain experts are essential to maximize the potential of AI in drug discovery.

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Top 3 use cases of AI in Drug Discovery market:

• Drug Discovery and Design: AI can significantly accelerate the process of drug discovery by efficiently screening vast libraries of compounds and predicting their properties. AI algorithms can analyze large amounts of data, including chemical structures, biological activity, and toxicity profiles, to identify potential drug candidates with higher accuracy and speed than traditional methods. This use case allows researchers to explore a wider range of possibilities and prioritize the most promising candidates for further development, ultimately reducing the time and cost required to bring new drugs to market.
• Target Identification and Validation: Identifying and validating suitable drug targets is a critical step in the drug discovery process. AI algorithms can analyze complex biological data, such as genomics, proteomics, and clinical data, to identify potential targets and elucidate their biological mechanisms. By integrating diverse data sources and leveraging machine learning techniques, AI can uncover novel drug targets, validate their relevance to specific diseases, and predict the likelihood of success in drug development. This use case enables researchers to focus their efforts on targets with a higher probability of therapeutic success.
• Personalized Medicine and Precision Healthcare: AI has the potential to revolutionize personalized medicine by integrating patient data, such as genetic information, clinical parameters, and lifestyle factors. AI algorithms can analyze this data to identify patient subgroups, predict individual responses to therapies, and optimize treatment strategies. By tailoring treatments to individual patients based on their unique characteristics, AI enables precision healthcare approaches that improve treatment outcomes, minimize adverse effects, and optimize patient care. This use case has the potential to transform the way diseases are diagnosed, monitored, and treated, leading to more effective and personalized therapeutic interventions.

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