Abzu
AI-Powered RNA Therapeutics Design
Description
Abzu utilizes an AI-powered technology suite focused on enhancing the design of compounds for RNA therapeutics, including siRNAs, anti-miRs, and ASOs. By employing a data-driven drug design process, the platform aims to deliver higher quality designs tailored to specific research needs, maximizing the likelihood of successful drug development by focusing testing efforts on the most promising candidates.
The core of Abzu's offering involves predictive models trained and validated on curated data, considering factors such as thermodynamics, genetic variation, and various drug properties often overlooked by traditional methods. This approach, leveraging their proprietary explainable AI, the QLattice®, allows for the prediction of critical drug properties like activity, cross-reactivity, and off-target potential. The outcome includes ready-to-use compound designs and detailed reports, with options to incorporate existing experimental data or utilize Abzu's end-to-end service from design through in-vitro validation.
Key Features
- AI-Powered Compound Design: Designs optimized siRNAs, anti-miRs, and ASOs using predictive models.
- Explainable AI (QLattice®): Utilizes proprietary explainable AI for transparent modeling and insights.
- Predictive Modeling: Employs models to predict key drug properties like activity, cross-reactivity, and off-target potential.
- Data-Driven Approach: Leverages curated datasets and proprietary models, with options to incorporate user data.
- One-Stop Solution Option: Offers optional services from initial design to in-vitro validation.
- High-Quality Data Focus: Emphasizes high-standard data generation and curation for reliable AI modeling.
Use Cases
- Designing active and safe siRNA therapeutics.
- Developing optimized ASO (Antisense Oligonucleotide) compounds.
- Identifying promising anti-miR candidates.
- Predicting drug properties to guide compound selection.
- Reducing failures in early-stage RNA drug development.
- Accelerating the selection process for RNA therapeutic candidates.
- Integrating AI insights into existing drug discovery workflows.
