The Engine

The Multi-Omic Target ID suite integrates multiple AI-driven dashboards for comprehensive therapeutic target identification, combining sequence generation, genomics, transcriptomics, and proteomics data into a unified discovery framework. It bridges molecular-scale modeling and systems-level analysis, enabling researchers to design, predict, and validate targets across DNA, RNA, and protein layers. Through advanced machine learning models and scalable analytics, the suite connects sequence generation, structural prediction, variant analysis, and expression profiling to accelerate multi-omic discovery.

This system combines tools for sequence generation and protein modeling (EVO2), homology and evolutionary analysis (Multiple Sequence Alignment Search), variant calling (Genomics Analysis Pipeline), and cellular and transcriptomic profiling (Single Cell and Transcriptomics Dashboards), all accessible through the Revilico Command Center. Together, they provide a seamless path from molecular data input to validated, druggable targets.

The Algorithm

The Multi-Omic Target ID suite unifies several interdependent modules that each contribute a layer of biological understanding:

• EVO2 Sequence Generation: Uses NVIDIA’s EVO2-40B and ESMFold models to generate DNA sequences and predict corresponding protein structures. Supports automatic ORF detection, AI-guided design, and interactive 3D visualization for structural exploration and optimization.

• Multiple Sequence Alignment (MSA) Search: Identifies homologous sequences and conserved motifs using ColabFold’s MSA-search, enabling evolutionary and functional annotation. Alignments inform target conservation, structural homology, and potential cross-species validation.

• Genomics Analysis Pipeline: Processes paired-end sequencing data through a configurable pipeline for variant calling and reference-based analysis. Detects SNPs, insertions, deletions, and known variants, integrating these findings into target evaluation workflows.

• Transcriptomics and Single-Cell Dashboards: Perform RNA and single-cell RNA-seq analysis for expression profiling, cell clustering, and regulatory network discovery. Integrates pathway enrichment and target validation capabilities.

By linking sequence generation with omic-scale validation, the suite creates a vertically integrated pipeline for AI-driven target discovery.

Algorithm Validation

Each component within the Multi-Omic Target ID ecosystem is benchmarked against standard omics analysis frameworks: EVO2 and ESMFold models replicate structural accuracy comparable to experimental PDB benchmarks, while ColabFold’s MSA-search provides sequence alignments consistent with BLAST and UniRef datasets. The genomics and transcriptomics pipelines adhere to industry-standard variant calling and single-cell QC protocols, ensuring data integrity and reproducibility. Cross-validation across these layers, sequence, structure, and expression, ensures that identified targets are not only theoretically viable but biologically relevant and experimentally actionable.

Scientific Impact

The Multi-Omic Target ID suite allows researchers to explore therapeutic targets through a multidimensional biological lens:

• Generate and model novel protein-coding sequences with AI.

• Detect conserved motifs and homologs across evolutionary space.

• Link structural predictions with variant-induced functional changes.

• Identify expression shifts in cell populations or disease contexts.

• Integrate genomic, transcriptomic, and proteomic data to define target mechanisms.

This platform enables data-driven hypothesis generation, validation, and prioritization, empowering users to uncover previously hidden relationships between genotype, phenotype, and therapeutic response.

Business Impact

The Multi-Omic Target ID suite enhances R&D efficiency by unifying disparate analytical pipelines into a cohesive, automated system:

• Accelerates discovery by connecting raw sequencing data to actionable targets in one workflow.

• Improves decision quality by merging structural, genetic, and transcriptomic evidence into a single framework.

• Reduces experimental overhead through predictive modeling and in silico validation.

• Supports scalable collaboration across bioinformatics, structural biology, and drug development teams.

By fusing AI-powered sequence generation, variant analytics, and cellular expression profiling, the Multi-Omic Target ID suite transforms large-scale biological data into a coherent foundation for next-generation target discovery and precision therapeutics.