The number of AI-native drug discovery platforms has moved from a handful of pioneers to a crowded field spanning foundation models, generative chemistry, lab-in-the-loop automation and end-to-end discovery engines. For pharma R&D, business development and innovation teams, the question is no longer whether to adopt AI — it is choosing the right platform from vendors whose claims are difficult to compare and whose real capabilities often sit behind a confident demo. A wrong choice is expensive: wasted licence spend, stalled programs, and partnerships that never translate computational output into clinical candidates. This white paper replaces vendor narrative with a structured evaluation approach. It sets out five dimensions — Model Intelligence, Biological Data, Molecular Design, Clinical Translation and Commercial Scalability — against which any AI-native platform can be assessed on a like-for-like basis. Rather than ranking vendors, it equips your team to ask the questions that separate genuine discovery capability from well-packaged tooling, and to align platform selection with your portfolio, therapeutic focus and internal readiness.
Written for: Strategy, R&D, Business Development, Portfolio, Innovation and Digital Transformation teams in pharma and biotech — and investors assessing AI drug discovery assets.
These frameworks are how we start conversations with teams facing real decisions. Read it, use it in your own analysis — there's no obligation and no hard sell.
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