Collide Therapeutics creates AI‑driven small‑molecule activators of ribosome quality control pathways, such as the ZNF598 activator CLD‑002, to restore ribosomal function and prevent protein aggregation in neurodegenerative diseases. Their automated discovery and synthesis platform accelerates lead optimization for brain‑penetrant, disease‑modifying therapies targeting Alzheimer’s, Parkinson’s, ALS and related disorders.
Funding
Funding not disclosed
Founders
Product
Problem
Neurodegenerative diseases and age‑related decline are driven by failure of ribosome quality control (RQC), leading to ribosome pausing, protein aggregation, and proteostasis collapse. Existing therapies target downstream misfolded proteins, leaving the upstream RQC dysfunction unaddressed.
Solution
Collide Therapeutics develops small‑molecule activators of RQC pathways to restore ribosomal function and prevent nascent polypeptide aggregation. By leveraging AI‑driven target identification and fully automated discovery pipelines, the company rapidly moves from target validation to lead optimization with reduced cost and risk. Their lead candidate, CLD‑002, is a novel ZNF598 activator designed to enhance the cellular mechanism that resolves collided ribosomes. The approach targets the root cause of proteostasis impairment across multiple neurodegenerative conditions, aiming to provide disease‑modifying effects rather than symptomatic relief.
Target Audience
Primary customers are pharmaceutical and biotech companies developing disease‑modifying therapies for Alzheimer’s disease, Parkinson’s disease, ALS, and other age‑related neurodegenerative disorders.
Features
- AI‑native target discovery platform that identifies tractable RQC components such as ZNF598
- Automated, scalable small‑molecule synthesis and optimization pipeline accelerating lead generation
- Small‑molecule modality with <40% Tanimoto similarity to known chemotypes, ensuring novelty
- Lead optimization focused on pharmacokinetic and brain‑penetrant properties for central nervous system indications
- Integration with the broader scientific research ecosystem through open data contributions and preprint publications