Clinical-stage AI-enabled Therapeutic Assets: an Industry-Wide Analysis of Scale, Clinical Progression, and Company Characteristics.

11072 Background: Artificial intelligence (AI) is increasingly applied in therapeutic discovery and has driven substantial investment in AI-native biotechnology firms. However, the extent to which AI-enabled assets have progressed into and through clinical development remains incompletely characterized. We performed an industry-wide descriptive analysis of AI-enabled therapeutic assets that have entered interventional clinical trials. Methods: We identified AI-enabled therapeutic assets that entered at least one Phase 1–3 interventional clinical trial through July 1, 2025. Assets were manually curated using industry databases, public disclosures, and trial registries. AI enablement was defined at the asset level and required evidence of AI contribution to discovery or design. Drug-, trial-, and company-level characteristics were summarized using descriptive statistics. Company characteristics were obtained from PitchBook and public sources. Results: A total of 117 AI-enabled therapeutic assets across 63 companies entered interventional clinical trials. Oncology accounted for 69 assets (59.0%), and most assets were small molecules (96/117; 82.1%). As of December 1, 2025, 60 assets (51.3%) had completed Phase 1 and 8 (6.8%) had completed Phase 2. Among assets with reported Phase 1 enrollment (n = 103), the median sample size was 50 participants (IQR, 24–76.5). Approximately 35.9% of assets targeted a novel biological target. At the company level, the median time from founding to Phase 1 entry was 6.5 years (IQR, 4.0–9.2; n = 44). Median total funding was $186.7 million (IQR, $48.1M–$595.0M; n = 57), and median employee count at clinical entry was 73 (IQR, 32–129; n = 35). Median operational intensity was $2.72 million per employee (IQR, $1.71M–$5.83M; n = 47). Conclusions: AI-enabled therapeutic assets are increasingly entering clinical development, particularly in oncology, but most remain early in the clinical pipeline, with few having completed Phase 2. AI-native biotechnology companies that have reached the clinic typically do so within several years of founding with relatively small employee counts and high operational intensity. These findings provide a transparent baseline for evaluating the clinical impact of AI-enabled drug discovery as this cohort matures.