AI-Native Discovery Engines

Scientific Discovery,
Accelerated by AI

Autonomous discovery platforms that run closed-loop hypothesis-to-validation cycles. We compress years of drug, materials, protein, and genomics research into weeks.

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Autonomous Optimization Cycles

Trusted by leading research organizations

What We Do

We Don't Build Copilots.
We Build Discovery Engines.

Our platforms autonomously propose hypotheses, generate candidates, simulate outcomes, and refine results through closed-loop AI cycles.

Generate

AI proposes thousands of novel candidates — molecules, materials, or proteins — optimized for your target criteria.

Simulate

GPU-accelerated simulations evaluate each candidate's properties, binding, stability, and performance in milliseconds.

Optimize

Multi-objective optimization selects the best candidates and feeds insights back to the generator for the next cycle.

Validate

Top candidates are surfaced with full evidence packages, ready for lab synthesis with AI-planned experimental protocols.

Our Products

Four Platforms. Four Domains.
One Autonomous Loop.

Each product is a fully autonomous discovery engine built for a specific scientific domain.

Drug Discovery
XineDiscover logo

XineDiscover

Closed-loop AI drug discovery platform. Autonomously generates drug-like molecules, screens them against protein targets, predicts ADMET properties, and optimizes lead candidates.

Generative molecular design with scaffold-aware generation
GPU-accelerated virtual screening: 10M molecules/hour
Multi-task ADMET prediction with 85%+ accuracy
AI retrosynthetic route planning with cost estimation
Drug discovery laboratory
Materials science crystal structure
Materials Science
XineMaterials logo

XineMaterials

AI-native materials discovery platform. Generates novel compositions, predicts properties with physics-informed neural networks 1000x faster than quantum simulations.

Generative composition design with crystal-language models
Physics-informed property prediction (band gap, conductivity, strength)
Atomistic simulation for million-atom systems in hours
Digital twin manufacturing process simulation
Protein Engineering
XineProtein logo

XineProtein

AI-powered protein engineering platform. Designs novel proteins, predicts 3D structures in seconds, simulates stability and binding, and optimizes variants through autonomous cycles.

Protein language model for zero-shot mutation effect prediction
Structure prediction in seconds with sub-angstrom accuracy
Specialized antibody design: CDR optimization, humanization
GPU-accelerated molecular dynamics for stability validation
Protein structure visualization
Genomics DNA sequencing
Genomics & Gene Therapy
XineGenomics logo

XineGenomics

AI-powered genomics and gene therapy platform. Analyzes whole-genome sequences, designs CRISPR guide RNAs, optimizes gene therapy vectors, and predicts variant pathogenicity at scale.

Genomic foundation model trained on 500K+ whole genomes
CRISPR guide RNA design with off-target prediction
AAV capsid engineering and LNP formulation optimization
Clinical-grade variant interpretation with ACMG classification
Discovery Workflow

From Question to Candidate

Every Xineplus engagement follows a rigorous path from research objective to prioritized validation package.

Define Objectives

Translate scientific goals into measurable design criteria, constraints, and decision thresholds.

Run the Loop

Generate, simulate, score, and optimize candidates across hundreds of autonomous cycles.

Review Evidence

Inspect ranked candidates with uncertainty, explainability, and protocol recommendations.

Validate in Lab

Move the strongest candidates into synthesis, assay, or prototype testing with clear handoff data.

Computational Scale

GPU-native simulation and inference let teams evaluate orders of magnitude more hypotheses than manual workflows.

Scientific Rigor

Predictions are paired with confidence, provenance, and validation guidance so researchers can make accountable decisions.

Closed-Loop Learning

Every result feeds the next cycle, improving candidate quality and narrowing the search space continuously.

Use Cases

Built Across Scientific Domains

Lead Discovery

Find novel scaffolds, rank compounds, and package leads for medicinal chemistry review.

Battery Materials

Search electrolyte and electrode composition spaces against conductivity, safety, and cost.

Antibody Design

Optimize binding, developability, and humanization before committing to expression.

Gene Therapy

Design guides, vectors, and variant interpretation workflows for genomic programs.

Evidence Packages

Every Candidate Comes With Context

Xineplus does not hand researchers a black-box score. Each recommendation includes the assumptions, simulations, model confidence, and suggested next experiment.

Ranked candidates with multi-objective trade-off summaries
Simulation traces, model confidence, and failure-mode alerts
Export-ready protocols for synthesis, assay, or prototype validation
Scientific evidence dashboard
Trusted By

Built for the World's Most
Ambitious Research Teams

"XineDiscover compressed our hit identification timeline from 14 months to 3 weeks. The closed-loop optimizer found scaffolds we'd never have explored manually."

Dr. Sarah Kim
Dr. Sarah Kim
VP Discovery, NovaBio Therapeutics

"We screened 50,000 alloy compositions in a single week with XineMaterials. Three candidates are now in prototype testing — that would have taken us 2 years traditionally."

Prof. Marco Rossi
Prof. Marco Rossi
Director, Advanced Materials Lab, ETH

"XineProtein's antibody design suite generated 12 high-affinity binders in the first campaign. 4 of them passed all developability filters. That's a 33% hit rate."

Dr. James Liu
Dr. James Liu
CSO, Immunex Biologics

Ready to Accelerate Your Discovery?

Join the research teams already using Xineplus to compress years of R&D into weeks.

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