Hengrui Pharma
Location: Shanghai
Track 1: Small Molecule CADD
Lead structure-based drug design: Perform molecular docking, virtual screening, and binding mode analysis using Schrödinger/MOE.
Optimize lead compound
Develop ADMET prediction models
Track 2: AI Drug Discovery (AIDD) Develop AI molecular generation tools: Generate novel compound structures with Diffusion/RL/GAN models Predict molecular activity and ADMET using Graph Neural Networks/Transformers Build data-driven pipelines: Train customized AI models by integrating ChEMBL/patent data
Track 3: Macromolecular & Target Discovery Macromolecular structure modeling: Predict protein/antibody structures with AlphaFold3/Rosetta Design PPI inhibitors/degraders (PROTACs) Omics data mining: Discover novel targets and biomarkers through multi-omics integration analysis
Basic Requirements Education: MS/PhD in Computational Chemistry, Bioinformatics, Computer Science, AI, or related fields Collaboration Skills: Ability to clearly interpret computational results for chemistry/biology teams and guide experimental design
Preferred Qualifications Tool Development: Proficiency in Python for scripting automation (e.g., MD analysis, AI model deployment) Industry Experience: Demonstrated tangible outcomes in drug discovery projects (e.g., advancing compounds to Hit-to-Lead stage) Technical Versatility: Understanding of cross-domain integration (e.g., utilizing AlphaFold3 outputs for molecular docking)
Track 2: AI Drug Discovery (AIDD) Develop AI molecular generation tools: Generate novel compound structures with Diffusion/RL/GAN models Predict molecular activity and ADMET using Graph Neural Networks/Transformers Build data-driven pipelines: Train customized AI models by integrating ChEMBL/patent data
Track 3: Macromolecular & Target Discovery Macromolecular structure modeling: Predict protein/antibody structures with AlphaFold3/Rosetta Design PPI inhibitors/degraders (PROTACs) Omics data mining: Discover novel targets and biomarkers through multi-omics integration analysis
Basic Requirements Education: MS/PhD in Computational Chemistry, Bioinformatics, Computer Science, AI, or related fields Collaboration Skills: Ability to clearly interpret computational results for chemistry/biology teams and guide experimental design
Preferred Qualifications Tool Development: Proficiency in Python for scripting automation (e.g., MD analysis, AI model deployment) Industry Experience: Demonstrated tangible outcomes in drug discovery projects (e.g., advancing compounds to Hit-to-Lead stage) Technical Versatility: Understanding of cross-domain integration (e.g., utilizing AlphaFold3 outputs for molecular docking)