T-Drug
Hit discovery powered by AI, Big Data & Systems Biology.
Accelerating Hit Discovery with AI-Driven, Systems Biology-Powered Drug Discovery
T-Drug accelerates drug discovery by integrating systems biology with AI-powered analytics, reducing costs and development time. By leveraging signature-based, network-based, and structure-based methods, T-Drug captures the complexity of biological systems to identify high-potential drug-target interactions. This multi-modal strategy enables a precise, scalable, and data-driven approach to hit discovery, uncovering novel therapeutic opportunities with greater efficiency.
Extensive Drug Screening Library: A Comprehensive Resource for Discovery
T-Drug’s extensive drug screening library features over 33,000 drugs and 5,000 targetable genes across 31 human cell lines from 17 tissues, providing a powerful resource for researchers and clinicians. This diverse collection supports data-driven decision-making, allowing for the exploration of a wide range of compounds and optimizing drug selection for various applications. With broad coverage across therapeutic areas, T-Drug enhances discovery and innovation, accelerating breakthroughs in drug development.
Smart Prioritization: Confidence-Driven Drug-Target Ranking
T-Drug simplifies drug discovery by ranking predicted drug-target pairs based on confidence scores, helping users quickly identify the most promising candidates—even without bioinformatics expertise. For deeper insights, prioritization can be refined using various hit criteria, such as hit rate, correlation strength, and target relevance. Whether streamlining selection or conducting a detailed technical analysis, T-Drug provides a flexible, data-driven approach to accelerate breakthroughs.
17 Tissues
31 Human Cell Lines
5K+ Targetable Genes
30K+ Drugs
Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach
We used computational drug repositioning to identify new treatments for ccRCC, selecting BUB1B, RRM2, ASF1B, and CCNB2 as key targets. Three repurposed drugs per target were identified and validated in vitro, showing reduced protein levels and inhibited cell viability. Our findings demonstrate the power of data-driven approaches in precision oncology.
Case Study: Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning
We identify PKLR as a key regulator of NAFLD and reposition small-molecule inhibitors through a computational drug discovery pipeline, validating their efficacy in preclinical models.
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