About

Neurovore builds AI that designs drug and food candidates and turns them into robotics ready scalable products Unlike point tools that stop at an in silico hit we connect discovery to manufacturability and unit economics from day one Our platform generates small molecule SMILES and protein and peptide candidates scores them for efficacy and safety then optimizes formulation and process plans under cost throughput and carbon constraints We serve biopharma research and development alternative protein and functional ingredient companies and organizations operating in constrained settings where automation shelf life and mass and volume budgets matter.

Solution & Product

Neurovore links discovery to design to manufacture Users start from a target scaffold or functional profile and our models propose candidate molecules or sequences We run in silico scoring then a multi objective optimizer searches for candidates that best balance performance uncertainty and cost The output is not just a ranked list Neurovore produces robotics ready process plans and closes the loop Delivery is API first SaaS modules PharmaAI ProteinAI FoodAI and DataHub with optional VPC or on prem deployments

Innovation & Scalability

Innovation a unified science and manufacturing brain We combine a data fabric and knowledge graph covering assays literature patents suppliers bill of materials and process data with hybrid retrieval and generation using transformers diffusion and uncertainty aware scoring Instead of optimizing molecules and proteins in isolation we optimize efficacy safety manufacturability throughput and cost and translate results into actionable automation ready process plans Governance is built in with provenance audit trails experiment tracking and model cards to support regulated environments Scalability comes from modular multi tenant SaaS and API metering enabling land and expand from pilots to enterprise rollouts

Traction & achievements

Product Core architecture defined Initial SMILES and sequence generators and property predictors running Design to manufacture scoring schema drafted Data Ingestion and normalization pipelines set up for assays literature patents and process and bill of materials data Early knowledge graph spine online Validation Private demos and benchmark runs completed Active discussions with labs foodtech and automation and robotics stakeholders for research use pilots and partner delivered wet lab validation Team building Advisor network forming across machine learning computational chemistry protein and food science and operations research Hiring planned for a Protein and Food Scientist to complete the loop from sequence design to bioprocess constraints

Team & Why Us

Founder and CEO Spiros Paraskevas AI systems and product strategy BioAI Engineer C Spyropoulos trains and evaluates generative chemistry and protein models including SMILES and sequence transformers graph neural networks and diffusion builds uncertainty and active learning loops and MLOps Computational Chemist Ch Bekiari SAR interpretation QSAR and ADMET modeling docking protocols assay curation and lab feedback loops Next hire Protein and Food Scientist to own functional sequence and blend design and bioprocess and packaging constraints Why us we combine AI chemistry and protein expertise and manufacturing and operations thinking enabling outputs that are immediately usable in automated research and development and production.

Funding Needs

Funding will accelerate Neurovore from a working platform to paid pilots and enterprise deployments. With our current core team covering AI systems, product and governance, BioAI modeling and MLOps, and computational chemistry, funding mainly increases speed and validation capacity. We aim to raise a pre seed or seed round of 1.2 million EUR, complemented by non dilutive grants. Priorities include key hires, a Protein and Food Scientist and an applied ML and optimization engineer, plus part time business development and partnership support. We will also invest in compute and data partnerships for training and benchmarking. We will execute pilots with partner labs and CROs, including limited wet lab validation and integrations with ELN and LIMS and robotics workflows. We will harden the product for regulated and data sensitive enterprise customers, including SSO and RBAC, audit logs, external security testing, and VPC and on prem packaging. Finally, we will invest in go to market to convert pilots into annual subscriptions and usage based APIs. Indicative budget split is approximately 60 percent team expansion, 20 percent pilots, integrations and wet lab validation, 10 percent compute and data, 5 percent security, compliance and VPC packaging, and 5 percent go to market and operations.