Platform

Our state-of-the-art computational drug discovery platform integrates advanced molecular dynamics simulations, structure-based virtual screening, and AI-driven ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling to revolutionize pharmaceutical research. Leveraging comprehensive chemical space exploration across ChEMBL, DrugBank, and ZINC databases containing over 100 million bioactive compounds, we employ quantum mechanics-informed docking algorithms and deep neural networks to predict drug-target interactions with exceptional precision. The platform incorporates AlphaFold-based protein structure prediction to enable high-resolution modeling of protein-protein and drug-protein interactions, facilitating the identification of novel binding sites and allosteric modulators.

Our next-generation AI/ML bioinformatics platform harnesses advanced deep learning architectures including transformers, graph neural networks, and convolutional neural networks for comprehensive genomic analysis and precision medicine applications. Leveraging multi-omics data integration from genomics, transcriptomics, proteomics, and clinical phenotypes across 10+ million biological samples, our proprietary algorithms achieve 92.4% accuracy in complex disease risk prediction and therapeutic response modeling. We employ state-of-the-art models including AlphaFold-inspired protein structure prediction, BERT-based genomic sequence analysis, and graph attention networks for pathway-level biological interpretation. The platform supports automated feature engineering, scalable model training, and hyperparameter optimization using distributed GPU clusters.

The platform supports automated spectral deconvolution, peptide identification, and label-free quantification, enabling high-throughput analysis of complex biological samples. Advanced normalization and batch correction workflows ensure robust cross-cohort comparisons, while integrated quality control modules monitor instrument performance and data integrity in real time. Our machine learning pipelines facilitate the identification of disease-specific protein and metabolite signatures, supporting early diagnosis, prognosis, and therapeutic monitoring across oncology, neurology, cardiometabolic, and rare disease applications. Multi-omics data integration links proteomic and metabolomic profiles with genomic, transcriptomic, and clinical phenotypes, providing a holistic view of molecular mechanisms and pathway perturbations.

Our advanced rare disease identification platform provides an end-to-end solution for the diagnosis of rare and ultra-rare genetic disorders, combining state-of-the-art genomics, AI-driven analytics, and clinical informatics. The platform supports comprehensive analysis of whole exome sequencing (WES), whole genome sequencing (WGS), and targeted gene panels, utilizing validated, clinical-grade bioinformatics pipelines that ensure high sensitivity and specificity in variant detection. Leveraging a multi-layered approach, our system integrates advanced variant calling algorithms, deep learning-based pathogenicity prediction, and automated ACMG-AMP guideline classification. Functional impact is assessed using ensemble scores (CADD, REVEL, VEST, SIFT, PolyPhen-2), while population frequency is cross-referenced with global databases such as gnomAD, ExAC, and 1000 Genomes to filter benign variants.