See how leading biotech, materials science, and protein engineering teams use Xineplus to compress discovery timelines from years to weeks.
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NovaBio needed novel scaffolds targeting KRAS G12C — one of oncology's most validated yet difficult targets. Traditional HTS campaigns had yielded only known chemotypes. XineDiscover's generative chemistry engine explored 12M virtual candidates and identified 8 structurally novel scaffolds with sub-micromolar binding affinity, all within 3 weeks of project kickoff.
A seed-stage rare disease startup had a promising hit compound but lacked the medicinal chemistry bandwidth to optimize it. XineDiscover ran closed-loop optimization across potency, selectivity, and ADMET properties simultaneously — delivering a development-ready lead series in 8 weeks at 60% lower cost than a traditional CRO engagement.
SolidPower was searching for a solid-state electrolyte with high ionic conductivity and electrochemical stability. XineMaterials screened 500K+ candidate compositions using physics-informed neural networks and identified a novel sulfide-based formulation achieving 5 mS/cm conductivity — a 3x improvement over their incumbent material — in just 10 weeks.
AutoAlloy needed a high-entropy alloy that could replace steel in structural automotive components without sacrificing strength. XineMaterials explored a 6-element composition space using GPU-accelerated thermodynamic simulation and multi-objective optimization, delivering an alloy with 30% density reduction while maintaining 800 MPa yield strength.
ImmunoX had a promising anti-PD-L1 antibody with moderate affinity that was being outcompeted by clinical-stage programs. XineProtein's directed evolution engine explored CDR sequence space using structure-guided generative models and molecular dynamics validation, achieving a 15x affinity improvement to 0.54 nM Kd while preserving manufacturability.
BioFuel Corp's cellulase enzyme lost activity above 55°C, limiting reactor throughput and increasing production costs. XineProtein identified stabilizing mutations across the enzyme's core and surface using evolutionary coupling analysis and free-energy perturbation, raising the optimal temperature by +22°C while cutting enzymatic production costs by 40%.
Every study compares AI-driven discovery against the customer's previous baseline for timeline, cost, and validation quality.
We document the historical workflow, cost structure, and decision gates before a campaign begins.
Each recommendation includes model versions, simulation inputs, and selection rationale.
Outcomes are assessed by customer lab results, partner assays, or prototype performance data.
Choose a target, property profile, or protein function with measurable success criteria.
Execute autonomous cycles and review evidence packages with the customer team.
Move shortlisted candidates into the lab or prototype process for confirmation.
Summarize outcomes in a customer-approved case study with transparent metrics.
"Xineplus didn't just accelerate our timeline — it fundamentally changed what we thought was possible. We went from a single hit compound to a development-ready lead series in weeks, not years."