The Gimerny AI Platform
Five integrated products that cover the full drug discovery pipeline, from target identification through clinical trial optimization. Each product stands alone, all work better together.
Request a DemoGimernyDiscover
AI-Driven Target Identification
GimernyDiscover transforms target identification from a years-long endeavor into a weeks-long process. By constructing massive biomedical knowledge graphs spanning protein-protein interactions, gene expression data, GWAS associations, and literature-mined relationships, Discover applies heterogeneous graph neural networks (HGNNs) to surface non-obvious target-disease connections. The platform integrates data from over 40 public and proprietary databases, cross-referencing genomic, transcriptomic, and proteomic signals to rank targets by druggability, novelty, and clinical translatability. Causal inference algorithms distinguish correlation from causation, while counterfactual reasoning modules predict the downstream effects of target modulation.
Core Technologies
Key Capabilities
Biomedical Knowledge Graph
A proprietary graph with 2.1B edges spanning proteins, genes, diseases, pathways, and compounds, updated weekly from 40+ data sources.
Heterogeneous GNN Engine
Custom message-passing neural networks that reason across node and edge types, capturing complex biological semantics that homogeneous models miss.
Causal Inference Module
Mendelian randomization and instrumental variable analysis to distinguish causal targets from mere correlations in observational data.
Druggability Scoring
Multi-factor scoring that evaluates binding site geometry, pocket dynamics, selectivity risk, and competitive landscape for each candidate target.
Literature Intelligence
NLP models trained on 35M+ PubMed abstracts and full-text articles, extracting structured relationships and identifying emerging targets before they trend.
GimernyGenome
Multi-Omics Intelligence Platform
GimernyGenome is a multi-omics analysis platform that breaks down data silos between sequencing, expression, and functional assay data. Built on a biological foundation model pre-trained on 4.2 petabytes of sequencing data, Genome learns universal representations of biological entities that transfer across organisms, tissues, and disease contexts. The platform supports single-cell RNA-seq, bulk RNA-seq, whole-genome sequencing, proteomics (mass spec), and metabolomics data, providing unified embeddings that enable cross-modal queries. Differential expression analysis, pathway enrichment, and cell-type deconvolution run in minutes rather than days.
Core Technologies
Key Capabilities
Biological Foundation Model
A 1.2B parameter transformer pre-trained on 4.2PB of multi-species sequencing data, learning universal biological representations.
Cross-Modal Embeddings
Contrastive learning aligns representations across genomics, transcriptomics, proteomics, and metabolomics into a shared latent space.
Single-Cell Analysis
Process up to 10M cells per run with automated cell-type annotation, trajectory inference, and perturbation response prediction.
Variant Effect Prediction
Zero-shot prediction of variant pathogenicity using evolutionary and structural features, outperforming CADD and REVEL on ClinVar benchmarks.
Federated Analysis
Run analyses across institutional boundaries without moving raw patient data, maintaining HIPAA and GDPR compliance by design.
GimernyMolecule
Generative Molecular Design
GimernyMolecule is a generative chemistry platform that designs novel molecular structures optimized for potency, selectivity, ADMET properties, and synthetic accessibility simultaneously. Unlike traditional virtual screening, which searches existing chemical space, Molecule generates entirely new scaffolds by operating in 3D conformational space using equivariant diffusion models. The platform generates molecules conditioned on target binding pockets (structure-based) or known active compounds (ligand-based), with a multi-objective reinforcement learning loop that balances competing design criteria. Every generated molecule is scored against physics-based free energy calculations and ADMET predictors before being prioritized for synthesis.
Core Technologies
Key Capabilities
3D Equivariant Diffusion
SE(3)-equivariant diffusion models that generate molecules in 3D space, respecting rotational and translational symmetry of molecular interactions.
Multi-Objective RL
Proximal policy optimization balances potency, selectivity, solubility, metabolic stability, and synthetic accessibility in a single generation loop.
Structure-Based Design
Pocket-conditioned generation that fills binding sites with optimal interactions, guided by molecular docking and MM-GBSA rescoring.
ADMET Prediction Suite
21 ADMET endpoints predicted simultaneously, including CYP inhibition, hERG liability, plasma protein binding, and blood-brain barrier permeation.
Synthesizability Scoring
Retrosynthetic-aware scoring ensures every generated molecule can be made in 3-5 synthetic steps using commercially available reagents.
GimernySynth
AI-Powered Synthesis Planning
GimernySynth closes the gap between computational design and laboratory reality. The platform takes any target molecule, whether designed by GimernyMolecule or drawn manually, and produces ranked synthesis routes with step-by-step instructions, reagent lists, and yield predictions. The core engine is a transformer model trained on 15M+ published reactions from the USPTO, Reaxys, and proprietary reaction databases. Unlike rule-based retrosynthesis tools, Synth learns reaction patterns end-to-end, capturing subtle regio- and stereoselectivity preferences that hand-coded rules miss. A Monte Carlo tree search algorithm explores the retrosynthetic space, pruning routes that require unavailable reagents or hazardous conditions.
Core Technologies
Key Capabilities
Transformer Retrosynthesis
A sequence-to-sequence transformer trained on 15M+ reactions that proposes single-step disconnections with >90% top-5 accuracy.
Route Search (MCTS)
Monte Carlo tree search explores millions of possible routes, scoring by step count, yield, cost, and environmental impact.
Yield Prediction
Graph neural networks predict reaction yield with <8% MAE, accounting for solvent, temperature, catalyst, and substrate effects.
Reagent Availability
Real-time integration with supplier catalogs (Sigma-Aldrich, Enamine, ChemBridge) ensures every proposed route uses purchasable starting materials.
Hazard Assessment
Automated flagging of routes involving explosive, highly toxic, or environmentally hazardous intermediates, with safer alternatives suggested.
GimernyTrial
Clinical Trial Intelligence
GimernyTrial applies machine learning to the most expensive phase of drug development: clinical trials. The platform ingests historical trial data, electronic health records, and real-world evidence to optimize every aspect of trial design. Patient stratification models identify subpopulations most likely to respond, reducing required enrollment by 30-40%. Digital twin simulations run thousands of virtual trials to test protocol variations before a single patient is recruited. Adaptive trial designs powered by Bayesian optimization adjust dosing, endpoints, and enrollment criteria in real-time based on interim data.
Core Technologies
Key Capabilities
Patient Stratification
Multi-modal classifiers combine genomic, clinical, and imaging biomarkers to identify responder subpopulations with 85% precision.
Digital Twin Simulation
Patient-level digital twins trained on 2M+ historical patient records simulate treatment responses under varying protocol conditions.
Adaptive Trial Design
Bayesian optimization dynamically adjusts dosing, arm allocation, and interim analyses to maximize statistical power while minimizing patient exposure.
Endpoint Selection
Causal models evaluate surrogate endpoints against long-term outcomes, identifying endpoints that accelerate approval timelines.
Site Selection
Predictive models rank clinical sites by enrollment speed, data quality, patient diversity, and geographic accessibility.
See the Full Platform in Action
Schedule a personalized demo with our team to see how the Gimerny AI platform can accelerate your drug discovery pipeline.