AIDD - (Princeton)

Location : Shanghai

Track 1 : Small Molecule CADD

Lead structure-based drug design : Perform molecular docking, virtual screening, and binding mode analysis using Schrdinger / 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)