Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Accelerate therapeutic and industrial innovation by moving from protein prediction to AI-driven de novo design.
Traditional protein engineering is a slow, trial-and-error process limited by natural sequence space. Our generative AI systems bypass this bottleneck, creating novel, stable, and functional protein sequences with desired properties from first principles.
We architect AI workflows that generate, simulate, and prioritize candidates, reducing discovery cycles from years to months and delivering lab-validated leads.
This capability is foundational for developing new therapeutics, industrial enzymes, and diagnostic tools. Explore our related work in Generative AI for Enzyme Engineering and AI for Biomolecular Structure Prediction.
Move beyond academic prediction to de novo creation of functional proteins. Our engineering service delivers validated, production-ready protein sequences that directly translate into therapeutic, industrial, and diagnostic applications.
Generate novel, stable protein candidates for biologics, enzymes, and vaccines in weeks, not years. We engineer sequences with optimized binding affinity, solubility, and immunogenicity profiles, de-risking your preclinical pipeline. Learn more about our approach to AI-driven drug discovery.
Design enzymes with enhanced catalytic activity, thermal stability, and substrate specificity for green chemistry and biomanufacturing. Our generative workflows create variants tailored to your process conditions, delivering measurable improvements in yield and efficiency. Explore our specialized generative AI for enzyme engineering.
Create entirely new protein-based biosensors and diagnostic reagents with high specificity and low cross-reactivity. We generate binding domains for novel epitopes or non-immunogenic tags, enabling next-generation point-of-care and lab-based assays.
Shift from expensive, iterative wet-lab screening to AI-driven in silico design. Our platform prioritizes synthesizable, expressible sequences with high predicted functionality, dramatically reducing the number of physical experiments required for validation.
Secure strong, defensible intellectual property with protein sequences that are novel, non-obvious, and have demonstrable utility. Our generative models explore vast, untapped regions of protein space to deliver compositions not found in nature.
A clear roadmap for developing a custom generative protein design system, from initial concept to a validated, production-ready AI model.
| Phase & Key Activities | Timeline | Core Deliverables | Client Involvement |
|---|---|---|---|
Discovery & Problem Scoping | 1-2 weeks | Technical requirements document, data readiness assessment, success metrics definition | Provide domain experts, access to legacy data, define target protein properties |
Data Pipeline & Featurization Engineering | 2-3 weeks | Cleaned, annotated training dataset, scalable ETL pipeline, molecular feature library | Approve data schemas, provide feedback on feature relevance |
Model Architecture Design & Initial Training | 3-4 weeks | Customized model architecture (e.g., ProteinMPNN/ESM-2 variant), initial performance benchmarks | Review architectural choices, validate biological plausibility of early outputs |
Iterative Optimization & In-Silico Validation | 4-6 weeks | Fine-tuned model, stability/functionality predictions, generated sequence library (1000s of candidates) | Prioritize candidate sequences for wet-lab testing, provide feedback on failure modes |
Deployment & Integration (MLOps) | 2-3 weeks | Containerized inference API, model monitoring dashboard, integration documentation | Provide staging environment, approve deployment architecture |
Ongoing Support & Model Refinement | Ongoing (Optional SLA) | Monthly performance reports, retraining pipelines, access to model updates | Share wet-lab validation results, identify new design objectives |
Our generative protein design engineering services translate advanced AI into validated, functional proteins that accelerate R&D timelines and de-risk therapeutic and industrial development. We focus on delivering lab-ready sequences with measurable outcomes.
De novo generation of stable, high-affinity antibody candidates, cytokine variants, and enzyme therapeutics with optimized pharmacokinetic properties. We deliver sequences pre-validated for expression yield and structural stability, reducing early-stage discovery cycles from months to weeks.
Learn more about our approach to AI-Driven Drug Discovery Platform Development.
AI-driven engineering of enzymes for enhanced catalytic activity, thermostability, and substrate specificity in biomanufacturing, bioremediation, and green chemistry. Our models predict mutations that achieve target performance metrics, bypassing costly high-throughput screening.
This work is complemented by our dedicated Generative AI for Enzyme Engineering service.
Design of highly specific protein binders and reporters for point-of-care diagnostics, in vivo imaging, and environmental monitoring. We generate proteins with tailored binding kinetics and fusion compatibility for integration into sensor platforms.
Generation of entirely novel protein folds and scaffolds not found in nature, creating blank-slate platforms for multi-specific biologics, drug delivery vehicles, and advanced biomaterials. This unlocks functionality beyond the constraints of natural protein families.
Computational design of immunogens that elicit potent and broad neutralizing antibody responses against viral pathogens and cancers. Our AI models optimize for structural mimicry of native epitopes while enhancing stability and manufacturability.
Rational design of peptides and miniproteins that disrupt pathogenic protein-protein interactions (PPIs) considered 'undruggable' by small molecules. Our generative models explore conformational space to identify tight-binding interfaces.
This often leverages insights from our Graph Neural Network Solutions for Biological Networks.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Common questions from CTOs and R&D leaders about partnering with Inference Systems for generative protein design projects.
Our standard engagement delivers a shortlist of high-confidence, novel protein sequences in 4-6 weeks. This includes data pipeline setup, model fine-tuning or training, and in-silico validation (stability, solubility, function prediction). Lab validation partnerships extend the timeline based on experimental throughput, but we architect the computational workflow for rapid iteration.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
