Rapidly screens millions of chemical compounds against a biological target to predict binding affinity and identify potential hits.
Employs generative AI models to propose novel, synthetically accessible chemical structures optimized for specific properties like potency, selectivity, and drug-likeness.
Predicts Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties for candidate molecules early in the discovery process.
Ingests and analyzes diverse biological data types, including genomics, transcriptomics, and proteomics, to identify and validate novel drug targets.
Provides a secure, cloud-based environment where distributed research teams can share data, run analyses, annotate results, and track project progress.
A medicinal chemist at a biotech startup uses ZebiAI to perform virtual high-throughput screening on a novel enzyme target. By uploading the target structure and screening a diverse compound library, the AI quickly ranks thousands of molecules by predicted activity. This allows the team to identify a handful of promising chemical starting points (hits) within days, bypassing months of expensive and low-yield experimental screening.
A project team in a mid-sized pharmaceutical company has a lead compound with good potency but poor metabolic stability. Using ZebiAI's generative design and ADMET prediction tools, they generate and evaluate thousands of structural analogs. The AI suggests specific chemical modifications that are predicted to improve metabolic stability while maintaining potency, guiding synthetic efforts and accelerating the optimization cycle.
Academic researchers investigating a complex disease like Alzheimer's integrate public genomic and proteomic datasets into ZebiAI. The platform's AI models analyze the data to identify novel proteins or pathways implicated in the disease pathology. Researchers then use the tool to prioritize these targets based on 'druggability' predictions, forming a strong hypothesis for a new therapeutic intervention before embarking on lab experiments.
A research hospital aims to find new uses for approved drugs (drug repurposing). They use ZebiAI to computationally screen a library of safe, marketed compounds against a new viral target. The AI predicts several existing drugs with potential off-target activity, providing a fast-track, low-risk development pathway for a new treatment, as safety profiles are already established.
A tool discovery group needs a highly selective chemical probe to study the function of a poorly characterized protein in cell biology. Using ZebiAI's generative design focused on selectivity filters, they design novel compounds predicted to bind exclusively to the target of interest. This enables the creation of precise research tools that reduce off-target effects in experimental models, leading to more reliable biological insights.
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