AI-Powered CRISPRDecision Intelligence

BioG-X1.0 evaluates and prioritizes gene-editing strategies across multiple CRISPR editors. A structured decision-support system that integrates strategy selection, efficacy scoring, and transparent reasoning.

Early-stage prototype under active development

What BioG-X1.0 Does

BioG-X1.0 implements a modular decision pipeline:

•Accepts a target sequence or gene input
•Evaluates multiple CRISPR editing approaches
•Applies structured scoring across defined parameters
•Compares strategies across different editors (Cas9, Base, Prime)
•Ranks the most suitable editing option
•Provides explanation for the decision

This is implemented as a modular prototype system with independent strategy, scoring, and explanation components.

The Challenge

CRISPR workflows are fragmented across multiple disconnected steps:

Target selection and validation
Guide RNA design and optimization
Off-target binding analysis
Therapeutic feasibility assessment

Each step uses different tools, data sources, and evaluation criteria. The real bottleneck isn't generating options—it's prioritizing them based on competing constraints: efficacy, safety, and practical deliverability.

System Overview

Input

Gene or sequence input

Strategy Engine

Generates candidate editing approaches

Scoring Layer

Evaluates efficacy and risk

Explanation Layer

Provides reasoning for selections

Output

Ranked editing strategies

Prototype & Progress

This is an early-stage prototype demonstrating the decision pipeline:

✓ Working Prototype

Functional MVP with core design logic and scoring framework implemented

✓ Modular Architecture

Strategy, scoring, and explanation modules operating independently

✓ Streamlit Interface

Local development environment for testing and iteration with research teams

✓ Preliminary Validation

Testing with standard CRISPR benchmarks and synthetic targets

Platform Vision

Our long-term vision is an editor-agnostic platform that integrates across CRISPR modalities:

Editor-agnostic design for multiple modalities (Cas9, Base Editing, Prime Editing)
Modular and extensible architecture for future integration
Integration with experimental and literature data for continuous improvement

Interested in Collaborating?

We're actively seeking feedback from researchers, gene-therapy companies, and clinical teams.