WHAT WE ARE BUILDING

Revolutionizing Biotech with Innovative Science

Obio is creating a machine learning-powered discovery engine to facilitate the design and validation of lipid nanoparticles (LNPs) for RNA and gene therapy applications. Instead of experimentally screening thousands of molecular combinations, our platform prioritizes promising candidates via predictive scoring, enabling teams to accelerate progress and concentrate efforts strategically. Designed for adaptability, the system evolves through real-world experimental feedback, broadening its applicability across diverse payloads, delivery scenarios, and therapeutic objectives.

Identifying the Key Constraint

Lipid nanoparticles (LNPs) represent a cornerstone technology in nucleic acid delivery, especially for mRNA vaccines and gene therapies. However, designing effective LNPs remains a slow, costly, and unpredictable endeavor.

Traditional trial-and-error methods struggle to meet escalating therapeutic demands, often necessitating multiple wet-lab iterations that incur hundreds of thousands of dollars and span several months.

What We Aim to Deliver

Novel Structural Exploration

Our platform extends beyond established LNP frameworks, producing chemically unique candidates that are challenging to derive through manual design or exhaustive screening.

Smarter Candidate Selection

Direct experimental resources toward high-potential LNP designs, ranked by predicted properties and historical performance data.

Fewer Experimental Cycles

Minimize design-test iterations by initiating with superior proposals, hastening the path to viable formulations.

Adaptability Across Contexts

Continuously refine through feedback, enabling the system to adjust to emerging payloads, biological environments, and delivery hurdles.

Early Results & Signal

In preliminary internal evaluations, our platform successfully replicated commercial LNPs solely through in silico generation and scoring, without requiring wet-lab retraining.

Predicted efficacy closely matched documented experimental outcomes, underscoring the system’s proficiency in prioritizing viable candidates.

molecular generation reinforcement learning by epoch _umap_animation

molecular generation reinforcement learning by molecule family through epoch _umap_animation

Our model autonomously designs novel molecules, distinct from modifying existing structures. Through iterative learning, it progressively prioritizes ionizable lipids (ILs), the critical components of effective lipid nanoparticles (LNPs). This adaptive focus demonstrates the system’s capability to discern valuable molecular patterns and converge on high-potential candidates without requiring explicit labeling or wet-lab validation.

LET'S EXPLORE TOGETHER

We are collaborating with scientists and biotech teams to validate novel LNPs and advance delivery innovations.

Our objective is to jointly test cutting-edge LNP designs, incorporate experimental insights, and expand the frontiers of drug delivery.

Interested in partnering?

We would be delighted to connect.

Obio Science