Kallyope
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.
System-wide in silico protein-ligand interaction modeling for precision drug discovery.
Moleculomic (formerly Moleculomics) represents a paradigm shift in pharmaceutical R&D by providing a platform for massive-scale, in silico protein-ligand interaction modeling across the entire human proteome. Its architecture is built upon a proprietary structural bioinformatics engine that moves beyond traditional docking by simulating how small molecules interact with virtually every known human protein structure simultaneously. This systemic approach allows researchers to identify not only potential therapeutic leads (hits) but also off-target effects that could lead to clinical toxicity, effectively front-loading the attrition process in drug development. By 2026, the platform has integrated advanced deep learning transformers for protein folding and binding affinity prediction, enabling high-fidelity simulations that were previously computationally prohibitive. The platform's utility spans from de novo drug design and drug repositioning to the assessment of genetic variants on drug efficacy, making it a critical asset for personalized medicine and precision toxicology. It bridges the gap between high-throughput screening and clinical validation, reducing the time-to-market for novel therapeutics by providing a comprehensive 'digital twin' of the human biochemical environment.
Simultaneous docking of a ligand against the entire library of human protein structures using massively parallel processing.
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.
Accelerating drug discovery through an end-to-end generative AI pipeline for target identification, molecular design, and clinical trial prediction.
The industry-standard interactive visualization tool for integrated exploration of large-scale genomic datasets.
Unlocking the causal biology of disease through Gemini Digital Twins.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Analyzes how single nucleotide polymorphisms (SNPs) alter the binding pocket structure and ligand affinity.
Models the downstream effects of binding across metabolic and signaling pathways.
Utilizes graph neural networks (GNNs) to predict binding affinity with higher accuracy than traditional physics-based force fields.
Integrated pipeline for predicting physicochemical properties and toxicity (hERG, CYP inhibition, etc.).
Uses machine learning to identify cryptic binding pockets that are not visible in static crystal structures.
Kubernetes-based orchestration that scales compute resources dynamically based on simulation complexity.
A lead compound shows high efficacy but researchers fear liver toxicity.
Registry Updated:2/7/2026
Modify the compound structure to eliminate liver enzyme affinity while maintaining target potency.
Finding existing FDA-approved drugs that could inhibit a specific viral protein.
Finding novel chemical scaffolds for a difficult target like a G-protein coupled receptor (GPCR).