Clinical-stage biotech platform for AI-driven cancer drug discovery
TargetRx applies machine learning (Python, PyTorch, TensorFlow, XGBoost) to small-molecule drug discovery, with active projects spanning AI model development and internal platform building. The hiring mix—skewed toward research and manufacturing roles, with accelerating velocity in China—reflects a company scaling from R&D into clinical validation and manufacturing readiness. Pain points around AI adoption and rapid candidate discovery suggest they're still operationalizing ML within traditional pharma workflows.
TargetRx is a clinical-stage pharmaceutical R&D company based in Shenzhen, founded in 2014, developing targeted therapies for cancer patients resistant to existing treatments. The company combines computational chemistry (Gaussian, Gromacs), high-performance computing, and machine learning to accelerate small-molecule design and screening. Across 51–200 employees, TargetRx has filed over 170 patents globally and maintains multiple compounds in multinational clinical trials. Current focus areas include AI-driven platform buildout, manufacturing scale-up, and sales deployment for partnership and licensing opportunities.
TargetRx uses Python, PyTorch, TensorFlow, and XGBoost for ML, Gaussian and Gromacs for computational chemistry, HPC infrastructure, HPLC and LC-MS for analytical chemistry, Power BI and SAS for reporting, and SAP S/4HANA for enterprise resource planning.
Primary projects include developing AI models for drug discovery, building an internal AI-driven discovery platform, patent trend analysis, and operational initiatives (budget planning, sales strategy deployment, HR digitalization, business process improvement).
Shenzhen TargetRx, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.