Xineplus is built on a vertically integrated AI stack purpose-designed for computational chemistry, materials science, and protein engineering. Every layer — from data ingestion to production inference — is optimized for scientific workloads.
Six foundational technologies power every Xineplus product, enabling researchers to move from hypothesis to validated result in days, not years.
Conditional generative models produce novel molecular structures, material compositions, and protein sequences that satisfy multi-constraint design specifications. Our models learn from millions of experimental data points to propose candidates with high predicted activity and synthesizability.
PINNs embed fundamental physical laws — thermodynamics, quantum mechanics, fluid dynamics — directly into the loss function of neural networks. This ensures predictions remain physically plausible even in data-sparse regimes, dramatically reducing the need for expensive experimental validation.
Molecular dynamics, density functional theory, and docking simulations run on massively parallel GPU clusters, achieving 100–1000× speedups over CPU-based workflows. We process billions of molecular interactions per second to rapidly evaluate candidate viability.
Real-world scientific design requires balancing competing objectives: potency vs. toxicity, strength vs. weight, binding affinity vs. stability. Our Pareto-optimal search algorithms explore trade-off surfaces to surface the most promising candidates across all dimensions simultaneously.
Pre-trained on vast corpora of scientific literature, experimental databases, and simulation outputs, our foundation models capture deep domain knowledge. Fine-tuning on customer-specific data yields state-of-the-art performance with minimal labeled examples, accelerating time-to-insight.
Low-latency model serving infrastructure delivers predictions in milliseconds, enabling interactive exploration and real-time feedback loops. Automated model versioning, A/B testing, and drift detection ensure production models remain accurate as data distributions evolve.
A vertically integrated stack designed for scientific AI workloads, from raw data to actionable insights.
Scientific discovery workloads are inherently parallel. GPU-native architecture doesn't just speed things up — it unlocks entirely new classes of computation that are infeasible on traditional infrastructure.
Traditional CPU-based pipelines evaluate candidate molecules sequentially. Our GPU-native approach processes tens of thousands of candidates simultaneously, enabling exhaustive search of chemical space that would take months on conventional hardware.
This isn't incremental improvement — it's a qualitative shift. Researchers can explore design spaces that were previously too large to consider, uncovering non-obvious solutions hidden in combinatorial complexity.
Enterprise-grade security is not optional when working with proprietary scientific data and intellectual property. Xineplus is built with security at every layer.
Independently audited controls for security, availability, processing integrity, confidentiality, and privacy. Annual recertification ensures continuous compliance with the highest industry standards.
Full HIPAA compliance for customers working with protected health information. BAA agreements, encrypted PHI handling, and audit trails meet the stringent requirements of pharmaceutical and biotech organizations.
Customer data is logically and physically isolated. Dedicated compute environments ensure your proprietary molecules, materials data, and protein sequences never co-mingle with other customers' data. All data encrypted at rest (AES-256) and in transit (TLS 1.3).
Every model run is grounded in traceable datasets, versioned transformations, and reproducible simulation inputs.
Scientific AI must be inspectable. Xineplus tracks model versions, confidence, and drift so teams can trust production use.
Every prediction records the model build, inputs, and inference configuration used.
Uncertainty estimates and applicability-domain checks flag candidates requiring caution.
New assay and validation data feed benchmark suites and model refresh decisions.
Connect candidates and assay results to laboratory information systems.
Export experiment plans, notes, and evidence packages for research records.
Sync approved datasets and outputs with enterprise analytics environments.
Integrate robotic lab and high-throughput instrumentation feedback loops.
Powering discovery at