Wrapping up 2025:  AI/ML drug discovery

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AI/ML in Drug Discovery & Development – Who Could Lead the Next Wave of Innovation?

Key insights 

• Currently, AI seems more effective in finding molecules than identifying targets, with its greatest strength in designing small and large molecules due to abundant training data
• Tempus appears to stand out in multiomics data integration, particularly in combining real-world clinical data with molecular information, outperforming competitors in this space
• Isomorphic Labs and Google DeepMind lead the field in protein structure prediction, with their models and team quality making them difficult to surpass in accuracy and capabilities
• Combination of Recursion-Exscientia appears logical, merging Recursion's screening capabilities with Exscientia's molecule design engine, but questions remain around target selection and pipeline synergies. Regarding Recursion's pipeline portfolio, specialist sees "lots of licensing, more licensing than I'd like"
• Expert predicts a wave of consolidation in the AI-driven drug discovery sector in 2025-26, as companies have to prove their systems can create molecules faster and cheaper than traditional methods

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AI/ML Drug Discovery & Development – Assessment of Iktos, Insilico, Latent Labs & Atomwise

Key insights 

• AI drug discovery companies struggle to differentiate themselves, as many claim proprietary technology but lack real data to prove superiority over competitors
• Insilico Medicine has a robust pipeline with total 31 programmes, including over 30 preclinical candidates, setting it apart from other AI drug discovery companies. However, such a robust pipeline risks negatively impacting focus while draining liquidity
• Iktos's focus on integrating generative AI with automated synthesis and testing to create a fully autonomous lab could potentially decrease drug discovery costs and increase efficiency
• Expert thinks Latent Labs' focus on atom-level frontier models for de novo protein binder design is a promising approach, but it's too early to evaluate without data
• Companies must narrow their focus and advance clinical candidates to prove the viability of their platforms and secure future funding

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AI/ML Drug Discovery & Development – Cradle Bio & Isomorphic Labs

Key insights 

• AI/ML drug discovery companies face increasing competition, with models such as Boltz-2, Chai and Latent-X claiming equal or superior performance to AlphaFold 3
• Expert argues that in silico approaches cannot fully replace wet lab research in drug discovery, as biological problems are complex and unpredictable
• AI/ML companies might be better at optimising existing small molecules across various characteristics than identifying new targets or mechanisms of action. Big pharma partnerships with AI companies might be more about publicity and marketing than genuine commitment to AI-driven drug discovery
• Expert believes Isomorphic Labs lacks a significant moat with AlphaFold 3, as competitors can replicate and enhance their technology relatively quickly
• Cradle Bio's business model of offering simple software licensing without royalties or milestones may not be sustainable in the long term due to replicability

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