CrisPRO.ai: In-Silico Research Framework
Accelerating drug discovery through AI fusion
An in-silico research-use-only (RUO) framework designed to accelerate drug discovery by fusing the capabilities of discriminative and generative Artificial Intelligence. Our platform orchestrates a generalist genome foundation model with a suite of specialist predictors and structural oracles to achieve state-of-the-art performance across multiple benchmarks.
SAE Biological Mastery
Virus Hunter
Identifies viral sequences and prophage regions
3D Folding Master
Predicts protein structure from 1D sequences
Genetic Surgery
Precise CRISPR guide RNA design and optimization
Universal Knowledge
Cross-species biological understanding
Complete AI Platform Integration
From Genetic Chaos to Therapeutic Clarity
The promise of precision medicine is fundamentally limited by our ability to interpret the functional consequences of genetic variation. CrisPRO.ai was conceived to address this challenge by creating an orchestration layer that combines a genome-scale foundation model with specialist predictors (e.g., AlphaMissense) and structure/epigenome oracles (e.g., AlphaFold 3, Enformer). The result is a system that can not only interpret the full spectrum of genetic variation but can also generatively design novel therapeutic constructs.
Meet the team
The people building CrisPRO — clinical, engineering, and design.
Fahad Kiani
Founder | Machine Learning Engineer | Full-Stack Engineer | AI Trainer
Fahad\n is a Senior Solutions Engineer and Founder with an interdisciplinary perspective that bridges software engineering, product development, and client acquisition. He holds a Bachelor of Science in Computer Science from John Jay College, bolstered by a background in Molecular Biology and a Full-Stack Software Engineering bootcamp certification from Per Scholas. This diverse academic and technical foundation enables him to tackle complex challenges with both analytical precision and innovative thinking.\nFahad’s career spans early-stage startups and large enterprises, cutting across industries such as healthcare, eCommerce, education, and data engineering. Armed with multiple AWS certifications, including AWS Solutions Architect – Professional and AWS Certified Machine Learning – Specialty, he excels at building user-friendly, secure, and cloud-native applications that leverage AI/ML to solve real-world business problems. \nCore Areas of Expertise\nFull-Stack Development\n: React, Node.js, Express, HTML/CSS, JavaScript/TypeScript\nCloud Architecture & DevOps\n: AWS Certified, CI/CD pipelines, Infrastructure as Code, Serverless (AWS Lambda), Containerization (Docker, Kubernetes)\nMachine Learning & AI\n: Model development and deployment, MLOps, natural language processing (NLP), predictive analytics\nData Engineering & Analytics\n: Data pipelines (ETL), data warehousing, big data processing, real-time analytics\nDatabase Design & Management\n: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB, DynamoDB) schema design, performance tuning\nAPI Development & Integration\n: REST, GraphQL, third-party integrations, microservices architecture\nSecurity & Compliance\n: Data privacy, application security best practices, regulatory compliance (HIPAA, GDPR)\nSEO & Marketing Optimization\n: Search engine optimization, performance tuning, digital marketing strategies\nProduct Management & Agile Methodologies\n: Roadmap planning, Scrum/Kanban, stakeholder collaboration, continuous improvement\n
- years
- 5+
- Students mentored
- 40+
- AWS Certifications
- 3+
- Succesful Acquisations
- 3+
Asim Abdul
Graphic Designer | Animations | UI/UX
Asim is a graphic designer, illustrator, and digital artist from Lahore, Pakistan. His work has led to the early development of Jedi Labs including the logo design. In addition, Asim worked across Jedi Labs to build creative designs for clients across logos, animations and creative work across eCommerce
- eCommerce Designs Created
- 10+
- Logos Developed
- 20+
- Animations Animated
- 10+
- Creative Stack Expertise
- 5+
Fusion Workflow: End-to-End Therapeutic Pipeline
Complete RUO workflow combining discriminative and generative AI for therapeutic discovery, from problem framing to validated designs ready for wet-lab validation.
Problem Framing & Data Curation
Assemble genomic loci, clinical variants, DMS datasets, and assay priors
Process Details
- Genomic loci identification
- Clinical variant collection
- DMS dataset integration
- Assay prior establishment
Expected Outputs
- Curated dataset
- Problem statement
- Success criteria
Target Assessment (Discriminative)
Score disease-relevant variants with CrisPRO.ai ΔLL and specialist ensemble
Process Details
- CrisPRO.ai zero-shot ΔLL scoring (8,192 bp context)
- AlphaMissense/GPN-MSA ensemble
- Noncoding and splice variant analysis
- Confidence score generation
Expected Outputs
- Variant scores
- Confidence metrics
- Evidence tiers
Mechanistic Triage & Hypothesis
Use CrisPRO.ai embeddings for exon/intron features and region ranking
Process Details
- CrisPRO.ai embedding analysis
- Exon/intron classification
- Motif feature extraction
- Perturbation region ranking
Expected Outputs
- Mechanistic insights
- Hypothesis ranking
- Feature importance
Design (Generative)
CrisPRO.ai sequence proposals with epigenomic guidance and structural validation
Process Details
- CrisPRO.ai sequence generation
- Enformer+Borzoi epigenomic guidance
- AlphaFold 3 structural validation
- Sequence naturalness screening
Expected Outputs
- Design candidates
- Structural models
- Epigenomic scores
In-Silico Validation
Aggregate scores and prioritize designs for wet-lab validation
Process Details
- ΔLL score aggregation
- Splice and regulatory AUROC
- Structure metrics (pLDDT/PAE)
- Pfam hit analysis
Expected Outputs
- Validation scores
- Priority ranking
- Minipool candidates
Feedback & Calibration
Fit supervised heads and calibrate by cohort for continuous improvement
Process Details
- Lightweight supervised head training
- CrisPRO.ai embedding calibration
- Cohort-specific adjustment
- Platt/isotonic calibration
Expected Outputs
- Calibrated models
- Performance metrics
- Updated thresholds
Reporting & Provenance
Generate evidence reports with traceable citations and audit trails
Process Details
- Evidence report generation
- Traceable citation linking
- Audit trail documentation
- RUO compliance verification
Expected Outputs
- Final report
- Provenance log
- Compliance certificate
Business Value: From Research to Revenue
Our results demonstrate that this fusion approach achieves 95.7% AUROC ClinVar validation on 53,210 samples, resolves 73% of Variants of Uncertain Significance (VUS), and provides a comprehensive, transparent, and controllable system for in-silico drug discovery.
Key Points
Accelerate R&D from years to weeks
Reduce experimental costs by $2.1M per program
Transform 40% VUS rate to 15% with validated predictions
Enable precision therapeutic design with predictable quality scaling
Provide comprehensive, transparent, and controllable system
Business Impact
Reduce experimental costs by $2.1M per program