Insights
AI Drug Discovery by the Numbers
Data-driven insights across tools, companies, trials, and modalities — visualizing the state of AI in drug discovery.
Most AI drug discovery tools remain in research stage — but the production tier is growing fast
Source: biotech.today tool database (60+ tools, Apr 2026) · practitioner-curated maturity assessment
Structure prediction leads with the most production-ready tools, anchored by AlphaFold2 and AutoDock Vina. Generative design and antibody engineering are maturing rapidly, with several tools graduating from research to production-grade in the past year. The amber Research+ tier represents tools with strong validation but pending broad industry adoption.
Small molecules dominate AI-discovered clinical candidates, but gene editing is punching above its weight
Source: ClinicalTrials.gov, company disclosures · AI-discovered / AI-optimised programs only · biotech.today analysis
Of the AI-discovered or AI-designed drugs currently in clinical trials, small molecules account for the majority — unsurprising given the maturity of computational chemistry. However, gene editing therapies have achieved the most advanced stages, with Casgevy (CRISPR) already FDA-approved. ADCs are emerging as a key modality with multiple Phase III programs.
2026 is already the biggest year for AI biotech deals, driven by mega-M&A
Source: Press releases, SEC filings, Crunchbase, PitchBook · AI-focused biotech only · biotech.today analysis
Q1 2026 alone saw more than $30B in deal value, led by Novartis–Avidity ($12B) and the Lilly–Insilico licensing mega-deal ($2.75B). M&A dominates by dollar volume, but VC activity remains robust with multiple $100M+ rounds. The partnership category is expanding as pharma companies secure platform access deals rather than acquiring outright.
Open-source tools cluster at Research+ maturity, while commercial platforms dominate production
Source: biotech.today tool database · axis positions illustrative (based on maturity + openSource + related-tool count)
This bubble chart maps tools by maturity (x-axis) and accessibility (y-axis), with bubble size reflecting ecosystem impact. The densest cluster sits at Research+ / high accessibility — open-source tools like Boltz-1, DiffDock, and ESMFold that are freely available but not yet production-validated. Production tools tend to split between open (AlphaFold2) and commercial (Schrödinger).
Structure prediction and generative design command the largest share of the tool ecosystem
91 tools tracked across 11 categories
Source: biotech.today tool database (Apr 2026)
The proportional breakdown reveals that protein structure prediction has the deepest tool bench, followed by generative molecular design and molecular docking. Newer categories like single-cell analysis and retrosynthesis are still nascent but growing. This distribution reflects where the most mature ML approaches have been applied in drug discovery.
Therapeutics lead on clinical validation; platforms lead on AI tool maturity
Source: biotech.today modalities database · composite scores from tool maturity, clinical data, regulatory history
This radar chart compares aggregate readiness across five dimensions for each modality category. Therapeutics score highest on clinical validation and regulatory pathways, while AI platforms excel in tool maturity and data availability. Enabling technologies occupy a middle ground, with strong commercial adoption but limited direct clinical validation.