Specialist
Director at US-headquartered pharmaceutical and biotechnology corporation
Agenda
- Recent trends and developments in the AI and machine learning space, highlighting potentially disruptive effects in pharmaceutical and biotech drug development space
- AI- and machine learning-differentiated language model capabilities, discussing long-term benefits in drug discovery, including clinical trial efficiency and decreased cost
- Integration pain points amid the shift to AI and machine learning, including restoring and extracting data, plus regulatory hurdles
- Consolidation dynamics between healthcare and tech companies to restore and extract proprietary data
- Outlook for how AI’s powerful capabilities can continue to transform healthcare globally
Questions
1.
Could you outline notable trends and developments taking place in the AI and machine learning market, more specifically its evolving penetration into drug discovery, causing the somewhat paradigm shift?
2.
You said we’re witnessing a continued and growing cross-industry alliance between AI, machine learning and drug discovery, and pharmaceutical and biotechnology companies are applying AI and machine learning to drug development and discovery. This is because the industry is facing pressures from the declining efficiency of traditional approaches. What are the positive disruptive effects associated with the convergence of AI, machine learning, drug discovery and biology compared to conventional drug development approaches?
3.
Based on some of the aspects that you outlined and the predictability factor associated with AI and clinical trials, AI can be used to reduce the risk of clinical failures in drug development. Is this correct?
4.
How can the use of AI and machine learning help to improve patient outcomes?
5.
How do companies balance the use of AI and machine learning with traditional methods in drug discovery and development? How can they be integrated with traditional R&D strategies to optimise the drug discovery and development process?
6.
What challenges do pharmaceutical and biotechnology companies face in adopting AI and machine learning technologies into their drug discovery and developmental process?
7.
Could you comment on the challenges related to validation of results generated by AI and machine learning models in drug development and discovery?
8.
How do you ensure reliability of AI algorithms?
9.
What are the risks associated with trust and reliance on AI?
10.
How do you see AI and machine learning impacting the role of human scientists and drug discovery and development, which is obviously going to change?
11.
In a previous Forum Interview, the specialist noted that in the near term there’s great value for the use of AI in the small molecule discovery process, and AI techniques will evolve to have a really good future in the development of biologics. Do you agree with this?
12.
What role do government agencies play in regulating the use of AI?
13.
As discussed, applying AI to drug discovery has the potential to revolutionise the way we develop new medicines, which has caused many players to emerge focusing on applying AI to various parts of the drug discovery and overall development process. Recursion, Valo, Insitro and BenevolentAI are several companies that leverage AI technology, machine learning and automation technology as a way to discover transformative new treatments. How are major players differentiated from eachother in terms of their technology and offerings?
14.
Novel alliances are being formed to help deploy AI and machine learning applications across large pharmaceutical companies. A notable alliance was formed in 2022 between Novo Nordisk and Microsoft to speed up Novo’s drug discovery work. How do large pharmaceutical companies identify and select the right AI-enabled technological partners for collaboration?
15.
Large pharmaceutical companies such as Novartis and GSK are substantially promoting internal efforts in AI. What factors influence a company’s decision to invest in internal AI technology development vs partnering with an external AI company?
16.
Is there anything else that you believe is particularly noteworthy concerning AI and machine learning?
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