Serna Bio
Serna Bio offers an AI‑driven platform that designs, synthesizes, and screens small‑molecule modulators of RNA translation and splicing using deep‑learning predictions and massively multiplexed cell‑based assays. The closed‑loop workflow delivers quantitative SAR and selectivity data, enabling pharmaceutical and biotech R&D teams to discover therapeutics for previously undruggable RNA targets.
- Artificial Intelligence
- Biotechnology
Funding
Funding not disclosed
+2Founders
Product
Problem
A large portion of the human genome remains undruggable because traditional discovery pipelines target proteins, leaving RNA‑mediated mechanisms of translation and splicing largely unexplored. Existing methods lack scalable, data‑driven approaches to identify small molecules that bind RNA with high selectivity, limiting therapeutic options for diseases with high unmet need.
Solution
Serna Bio’s platform leverages deep learning‑guided molecular design, synthetic biology, and massively multiplexed functional screening to create small‑molecule modulators of RNA translation and splicing. An AI engine generates candidate chemotypes predicted to engage specific RNA structures, then refines them using iterative machine‑learning models trained on high‑throughput binding and activity data. Synthesized compounds are evaluated in multiplexed cell‑based assays that quantify translational output and splice‑variant shifts, enabling rapid SAR cycles. The integrated data pipeline provides quantitative structure‑activity relationships and selectivity metrics, allowing rational design of ligands for previously inaccessible RNA targets. This end‑to‑end workflow accelerates preclinical candidate generation for high‑unmet‑need indications where RNA regulation is a key disease driver.
Target Audience
The primary customers are pharmaceutical and biotech R&D organizations, as well as academic translational labs, that seek to develop therapeutics targeting RNA‑mediated disease mechanisms, especially in areas with high unmet medical need.
Features
- Deep learning architecture that predicts RNA‑binding chemotypes based on secondary/tertiary structure motifs
- Automated synthetic biology pipeline for rapid on‑demand synthesis of candidate small molecules
- Massively multiplexed cell‑based assays measuring translation efficiency and splice‑isoform ratios in a single run
- Closed‑loop ML model that incorporates assay readouts to iteratively optimize potency, selectivity, and drug‑like properties
- Integrated data lake with FAIR‑compliant metadata, enabling cross‑target knowledge transfer and accelerated SAR analysis
- Cloud‑native analytics dashboard for real‑time visualization of binding affinity, functional activity, and off‑target profiles
- Compatibility with standard hit‑to‑lead workflows and API access for seamless integration into partner discovery pipelines