XineDiscover
XineDiscover is a closed-loop AI drug discovery platform that autonomously generates, screens, and optimizes drug candidates. It replaces the traditional 12-18 month hit identification process with an AI-driven cycle that runs in weeks.
The pharmaceutical industry spends $2.6 billion per approved drug, takes 10-15 years from lab to market, and watches 90% of candidates fail in clinical trials.
The cost of bringing a single drug to market continues to rise, with most of the expense in failed candidates and slow iteration.
Nine out of ten drug candidates that enter clinical trials fail — often due to problems that could have been predicted computationally.
Researchers juggle separate software for molecular generation, docking, ADMET, and synthesis planning — with no feedback loop between them.
Our AI runs the full design-screen-predict-optimize loop autonomously, surfacing validated lead candidates for human review.
Upload your protein target structure or specify the target by name. Set desired drug properties: molecular weight, LogP, selectivity constraints, and therapeutic area.
Our generative AI proposes 10,000+ novel drug-like molecules per cycle. Scaffold-aware generation ensures chemical validity and synthesizability from the start.
GPU-accelerated docking evaluates binding affinity. Multi-task GNNs predict ADMET properties. Every molecule gets a comprehensive scorecard in milliseconds.
Bayesian optimization selects top candidates and refines them over 50-100 autonomous cycles. Top 10 leads are surfaced with synthesis routes and evidence packages.
Our generative models create novel, drug-like molecules optimized for your specific target. Unlike virtual library screening, we explore chemical space that has never been synthesized before.
Our GPU-accelerated docking engine evaluates binding affinity at a scale that makes exhaustive screening practical. Every generated molecule is docked, scored, and ranked in real-time.
Our multi-task graph neural networks predict absorption, distribution, metabolism, excretion, and toxicity for every candidate — catching clinical-stage failures at the design stage.
Design and optimize molecules with AI assistance. Explore chemical space you'd never reach manually.
Run large-scale simulations and docking campaigns with GPU acceleration. Analyze results with built-in ML tools.
Monitor pipeline progress in real-time. Make go/no-go decisions backed by AI-generated evidence.
Receive synthesis-ready candidates with full documentation, retrosynthetic routes, and predicted yields.
XineDiscover packages every recommended lead with the evidence needed for medicinal chemistry and assay planning.
Retrosynthetic plans, reagent considerations, and route confidence for shortlisted molecules.
Recommended binding, selectivity, and ADMET assays based on candidate risk profile.
Multi-objective scores, docking poses, prediction confidence, and optimization history in one package.
Use XineDiscover as a focused pilot workspace, an API-driven discovery service, or a secured enterprise environment for multiple project teams.
A target structure, target name, known ligands, or assay objective is enough to scope a first campaign.
Yes. Campaigns can enforce molecular property ranges, scaffold preferences, novelty requirements, and excluded chemotypes.
Results can be exported as SDF, CSV, API responses, and human-readable evidence reports.