From Target to Lead: Computational Drug Discovery as a Service.
Our integrated platform combines deep neural network predictions, molecular dynamics simulation, and machine-learning ranking to accelerate hit identification, lead optimization, and drug repurposing across GPCRs, kinases, and the broader druggable proteome.
Our Discovery Services
End-to-end computational capabilities from target identification through lead delivery.
Forward Discovery
Gene to Drug
From target gene or protein to prioritized small-molecule hits using structure-based and ligand-based virtual screening.
Learn moreReverse Discovery
Drug to Indication
Identify novel indications for existing compounds or clinical-stage molecules via proteome-wide docking and pathway analysis.
Learn moreBiased Agonist Design
GPCR-Focused
Engineer functionally selective GPCR ligands that preferentially activate G-protein over β-arrestin pathways for improved therapeutic windows.
Learn morePeptide Binder Design
Structure-Guided
AI-guided cyclic and linear peptide design targeting protein–protein interfaces and allosteric pockets inaccessible to small molecules.
Learn moreADMET & Safety Profiling
In Silico Screening
Predict absorption, distribution, metabolism, excretion, and toxicity flags before committing to wet-lab synthesis.
Learn moreConfidence-Scored Ranking
Multi-Axis Output
Every deliverable includes five independent scores—Confidence, Novelty, Safety Risk, Repurposing Potential, and Experimental Priority.
Learn moreHow It Works
A streamlined four-step process from input to actionable discovery output.
Define Target or Compound
Provide a gene name, protein structure, or compound library. We intake FASTA sequences, PDB files, SMILES strings, or raw screening data.
Computational Screening
Our platform runs molecular docking, deep-learning affinity prediction, and molecular dynamics simulations across millions of candidates.
Validation & Ranking
Results are filtered through ADMET models, consensus scoring, and novelty analysis to surface the highest-priority compounds.
Deliverables & Report
You receive a ranked compound list, confidence scores, structural visualizations, and an interpretation report within the agreed timeline.
Define Target or Compound
Provide a gene name, protein structure, or compound library. We intake FASTA sequences, PDB files, SMILES strings, or raw screening data.
Computational Screening
Our platform runs molecular docking, deep-learning affinity prediction, and molecular dynamics simulations across millions of candidates.
Validation & Ranking
Results are filtered through ADMET models, consensus scoring, and novelty analysis to surface the highest-priority compounds.
Deliverables & Report
You receive a ranked compound list, confidence scores, structural visualizations, and an interpretation report within the agreed timeline.
Featured Service Details
Deep dives into three of our most-requested discovery programs.
Drug Repurposing Pipeline
Our drug repurposing pipeline systematically docks approved and investigational compounds against a curated set of 200+ disease-relevant targets using a tiered scoring approach that combines ensemble docking, binding free-energy estimation, and proteome-wide selectivity analysis.
Each compound is evaluated not only for binding affinity at the primary target but also for off-target liability, metabolic stability, and clinical translatability—producing a ranked shortlist ready for experimental confirmation.
Our platform has successfully identified repurposing candidates in oncology, cardiovascular disease, and rare genetic disorders, with several advancing to in vitro validation within our partner laboratories.
What You Receive
- Ranked list of top 10–30 repurposing candidates with Confidence Scores
- Docking pose visualizations for all shortlisted compounds
- ADMET flags and off-target selectivity heat map
- Full interpretation report with mechanistic rationale
- Priority recommendations for wet-lab follow-up
Drug Repurposing Pipeline
Sample result figure — to be replaced with platform output
Biased GPCR Agonist Discovery
Functional selectivity at GPCRs—preferential activation of G-protein signaling over β-arrestin recruitment—represents one of the most promising strategies for developing safer analgesics, cardioprotective agents, and metabolic therapeutics with reduced side-effect profiles.
Our biased agonist design workflow uses active-state homology modeling, conformational sampling via replica exchange molecular dynamics, and deep-learning-based efficacy prediction to identify or design molecules with measurable bias factors.
Deliverables include bias factor estimates, structural hypotheses for the molecular determinants of selectivity, and a prioritized compound series for synthesis or acquisition from commercial sources.
What You Receive
- Bias factor predictions (G-protein vs. β-arrestin) for candidate series
- Active-state receptor models with candidate binding poses
- Key pharmacophore features driving functional selectivity
- Suggested SAR modifications to enhance or tune bias
- Compound acquisition / synthesis priority list
GPCR Biased Agonist Design
Sample result figure — to be replaced with platform output
HFpEF & Cardiometabolic Target Validation
Heart failure with preserved ejection fraction (HFpEF) remains an area of high unmet need, with complex, overlapping pathophysiology spanning inflammation, metabolic dysfunction, and myocardial fibrosis. Our platform applies multi-omics integration and network pharmacology to identify and validate novel therapeutic targets in this space.
We overlay transcriptomic, proteomic, and metabolomic datasets from HFpEF patient cohorts with our druggability database to rank targets by therapeutic potential, novelty, and confidence of disease association.
This service is particularly suited for academic groups and biotechnology companies seeking to build IP around novel cardiometabolic targets before committing to high-cost in vivo programs.
What You Receive
- Target list scored for druggability, novelty, and disease confidence
- Network pharmacology maps linking targets to HFpEF pathways
- Candidate compound classes for each top-ranked target
- Literature and patent landscape summary
- Recommended experimental validation strategy
HFpEF Target Network Map
Sample result figure — to be replaced with platform output
Results & Case Studies
Real platform output across therapeutic areas — representative programs from our discovery portfolio.
Kinase Inhibitor Repurposing for Rare Pediatric Cancer
Starting from an FDA-approved kinase inhibitor library, our platform identified eight high-priority repurposing candidates for a pediatric solid tumor target with no approved therapy. Molecular dynamics confirmed stable binding modes for the top three candidates, which proceeded to cellular validation.
Biased GLP-1R Agonist Identification for Metabolic Disease
For a biotech partner targeting improved metabolic efficacy with reduced nausea, our GPCR biased-agonist pipeline screened 45,000 virtual compounds and delivered a ranked series of 12 peptide candidates predicted to show G-protein preference over β-arrestin recruitment at GLP-1R.
HFpEF Target Discovery from Multi-Omics Integration
Integrating transcriptomic datasets from four HFpEF patient cohorts with our druggability atlas, we identified and prioritized 15 novel targets enriched in cardiomyocyte energy metabolism and fibrosis pathways. Seven targets had no prior HFpEF-specific patent coverage.
Confidence Scoring System
Every prediction delivered by our platform is accompanied by five independent scores so that high-confidence + low-novelty (a known use case) is never confused with moderate-confidence + high-novelty (a potential new indication requiring more investigation).
This multi-axis framework enables research teams to immediately triage deliverables: pursue high-confidence, high-priority predictions first; flag high-novelty, moderate-confidence leads for orthogonal validation; and deprioritize high-safety-risk compounds regardless of binding score.
Each score is produced by an independent model trained on distinct data types—structural, chemical, bioactivity, clinical, and toxicological—minimizing correlated error and providing a robust picture of each candidate's true potential.
Sample Platform Output
Compound BT-22891
Target
GLP-1R (G-protein path)
Overall reliability of the binding prediction based on model ensemble agreement.
Chemical and mechanistic distance from known active compounds in public databases.
Predicted ADMET liability score — lower is safer.
Probability that the compound is already approved or in clinical trials for other indications.
Recommended urgency for wet-lab confirmation based on composite platform ranking.
All scores are computed independently. A high Repurposing score with moderate Confidence indicates a known use case under a new mechanism — not a validated prediction.
Engagement Models
Flexible partnership structures designed to fit academic, biotech, and pharma discovery programs.
Consultation
Single-Target Screening Report
Ideal for academic groups and early-stage biotechs seeking rapid in silico validation of a single target-compound hypothesis. We deliver a full screening report covering docking results, confidence scores, and ADMET flags — no long-term commitment required.
- Single target virtual screen
- Top 10–30 ranked candidates
- Confidence score report
- 30-minute debrief call
Collaboration
Multi-Target Program with Wet-Lab Feedback
A structured discovery partnership covering multiple targets or compound series over a defined program timeline. Iterative computational cycles are refined by experimental data from your laboratory or ours, enabling a tightly coupled in silico–in vitro feedback loop.
- Multi-target or multi-round screening
- Iterative refinement with wet-lab feedback
- Bi-weekly progress reports
- Dedicated project scientist
Sponsored Research
Embedded Program with Full IP Arrangement
A comprehensive research agreement in which our computational team is embedded into your drug discovery program. Covers target identification through lead series delivery, with full IP arrangement tailored to the partnership structure.
- Full program embedding
- IP arrangement included
- Target to lead optimization
- Publication & patent co-strategy
Research Use Disclaimer: All computational predictions delivered by this platform are research hypotheses. Clinical applications require experimental and clinical validation. Outputs are not medical advice and not a substitute for FDA-approved drug development pathways.
Ready to accelerate your discovery program?
Our team is available to discuss your research goals and recommend the right engagement model for your program.