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)
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