Identifying and validating the right biological target is the most critical decision in drug development. A well-chosen target with strong disease linkage and druggable properties can de-risk years of downstream investment, while a poorly validated target dooms programs regardless of compound quality. At Profacgen, our Target Identification & Validation platform integrates computational prediction, molecular interaction analysis, and functional genomics to deliver evidence-driven target nominations with quantified confidence.
Our approach combines in silico screening, physical interaction mapping, and cellular validation to build a multi-layered evidence base. Whether you are pursuing novel oncology targets, rare disease mechanisms, or repurposing opportunities, we provide the analytical rigor and experimental bandwidth to advance your program from concept to validated target with speed and precision.
Our in silico platform leverages advanced bioinformatics algorithms, deep learning architectures, and structural biology data to predict protein-protein interactions with high confidence. Services include binary interaction prediction (ISPPIsP), network reconstruction (ISPPINsP), and binding interface mapping (ISPPIsSP) to prioritize candidate targets and guide experimental validation strategies.
Physical interaction validation is essential for confirming computational predictions and establishing target engagement mechanisms. Our comprehensive PPI platform spans biochemical identification (Co-IP, pull-down, AP-MS, TAP-MS, SILAC-IP-MS, BioID), high-throughput screening (yeast two-hybrid, yeast display, protein microarray), and specialized analysis (membrane protein interaction, binding site mapping, ALPHA assay) to deliver robust, publication-ready interaction data.
Transcription factors, chromatin remodelers, and DNA repair enzymes represent high-value therapeutic targets whose function is defined by DNA binding. We employ ChIP-seq, EMSA, SPR, DAP-seq, and proximity ligation assays to map DNA-protein interactions with nucleotide-level resolution, revealing regulatory mechanisms and druggable interfaces for gene expression modulation.
RNA-binding proteins and non-coding RNA-protein complexes are increasingly recognized as critical regulators of gene expression and disease pathogenesis. Our platform integrates CLIP-seq, RIP-seq, RNA pull-down, and bioinformatics analysis to comprehensively map RNA-protein interaction networks, identifying novel targets for neurological disorders, cancer, and infectious diseases.
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