The Synthetic Oracle Workflow
From Raw Data to Validated Leads
Our multi-phase algorithmic process leverages generative intelligence and structural biology to accelerate drug discovery with unprecedented precision.
Phase 01
Transcriptomic Profiling & Preprocessing
We begin by ingesting massive raw TCGA datasets. Our proprietary pipeline executes high-fidelity exon-to-gene mapping and HVG (Highly Variable Gene) selection to isolate the most relevant biological signals for therapeutic intervention.
Automated TCGA Data Normalization
Precision Exon-to-Gene Mapping
Phase 02
Network-Based Target Prioritization
Utilizing co-expression network analysis, we detect gene modules associated with disease progression. Our system validates biomarkers through cross-referencing multi-omic repositories to ensure target druggability.
98%
Validation accuracy
50k+
Modules analyzed
Phase 03
Structural Modeling
We transition from genetic data to physical structure. By integrating AlphaFold PDB predictions, we define precise binding pockets and identify cryptic sites for allosteric modulation.
AlphaFold-2 Structural Integration
Binding Pocket Volumetric Analysis
Phase 04
De Novo Ligand Assembly
Our generative engine employs fragment-based assembly and evolutionary mutation algorithms. This creates novel chemical entities tailored specifically to the target's unique geometry.
Evolutionary Algorithms
Iterative mutation for optimal affinity
Phase 05
Physicochemical Profiling & Docking
Final candidates undergo multi-objective fitness screening. We utilize geometric docking and ADMET profiling to rank candidates based on safety, efficacy, and synthesis feasibility.
Multi-Objective Fitness Functions
High-Resolution Geometric Docking
