Cyclica
Cyclica is an AI-powered Science tool — AI drug discovery platform. Best for: Science. Pricing: Paid (GateOnAI Score: 46/100).
AI drug discovery platform
Provides AI‑driven target identification, off‑target prediction, and polypharmacology profiling for small‑molecule programs. The platform ingests protein structures, ligand libraries, and omics data to generate a ranked list of disease‑relevant targets. A built‑in molecular docking engine evaluates binding affinity across thousands of proteins in seconds. Users can run the “Proteome‑wide Interaction Map” to visualize potential side‑effects before synthesis. Results export as CSV or JSON for downstream analysis, and an API enables integration with existing LIMS pipelines. An integrated ADMET predictor flags metabolism liabilities and toxicity risks. Pharma R&D teams, biotech start‑ups, and academic drug‑discovery groups use Cyclica to prioritize targets early in the pipeline, cutting costly wet‑lab iterations. The platform supports lead‑optimization cycles by suggesting chemically tractable pockets and flagging liability hotspots. Subscription tiers grant access to a private cloud environment, versioned project workspaces, and dedicated support. Pricing is subscription‑only, with enterprise contracts that scale compute resources to match library size. Companies report faster hit‑to‑lead transitions and clearer safety hypotheses compared with legacy cheminformatics tools. The dashboard includes real‑time KPI charts for hit rate, selectivity, and predicted safety. Unlike generic QSAR services, Cyclica couples deep‑learning models with physics‑based docking, delivering both statistical confidence scores and structural rationales. The system can ingest proprietary assay data, keeping it behind corporate firewalls for compliance. However, the platform requires high‑quality protein structures; gaps in structural coverage reduce prediction depth. Users must also allocate internal bioinformatics staff to curate input datasets and interpret network maps. Cyclica also offers a collaborative notebook where chemists and modelers can annotate predictions together. Overall, the tool excels at expanding chemical space insight while demanding disciplined data preparation.
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Works Well With
Tools that Cyclica genuinely connects with, based on real input/output compatibility data (not just shared category):
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- IBM Research AI - IBM AI tools for scientific discovery
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