This week, we dive into the exciting advancements and discussions in AI-driven drug discovery, highlighting the potential and challenges of implementing AI in drug discovery.
🌟 AI's Promising Potential in Drug Discovery
Despite the hype and scepticism, AI holds significant promise if used correctly. Key to success are relevant and practical algorithms that generate stable, synthesizable compounds, realistic synthesis routes, and accurate predictive models. AI must show reproducible benefits across diverse projects, supported by compelling evidence. Tools should be robust, scalable, and user-friendly, allowing non-experts to use them easily. Adapting workflows and feeding AI with relevant data can significantly enhance decision-making and benefit drug discovery without replacing skilled chemists.
🏛️ FDA's AI/ML Guidance on the Horizon
The FDA plans to release draft guidance on AI/ML in drug development this year. This guidance, driven by feedback and reviews of over 300 AI-related submissions, aims to clarify AI applications from drug target identification to post-market safety surveillance. The focus is on ensuring strong evidence, accessibility, and global regulatory harmonization.
🤝 Merck & Biolojic Design Partner for AI-Driven Therapeutics
Merck KGaA, Darmstadt, Germany has partnered with Biolojic Design, Ltd. to develop new oncology and immunology therapeutics using AI, in a deal potentially worth up to 346 million euros. This collaboration focuses on creating multi-specific antibodies and antibody-drug conjugates (ADCs) to block tumour escape mechanisms and enhance safety and efficacy.
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