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
Network-Based Target Prioritization
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
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