AI专家 (AI药物设计)/AI Expert (AIDD)(博士)

1. Advancing AI & Computational Methodologies for ADCs

• ADC PK Prediction: Develop hybrid AI and mechanistic PK models (PBPK, QSP, compartment modeling) to predict ADC pharmacokinetic profiles, including clearance, half-life, plasma/circulation stability, cleavage kinetics and deconjugation rates.

• Linker-Payload Design & Multi-Parameter Optimization: Develop generative AI models, physics-based simulations, and computational frameworks for de novo Linker-Payload design, site-specific conjugation design, and structure-property relationship (SPR) modeling.

2. Cross-Functional Pipeline Enablement

• Targeted Project Support: Deploy bespoke AI/CADD solutions to address project-specific bottlenecks in ongoing ADC campaigns, accelerating linker-payload pairing, candidate selection, and optimization.

• Cross-Disciplinary Synergy: Collaborate closely with Biologics, ADC Chemistry, and DMPK teams to integrate experimental data feedback loops into AI model training and iterative molecule design.

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Education & Scientific Fundamentals

• Ph.D. in Computational Chemistry, Cheminformatics, Computational Biology, Pharmacometrics/PKPD Modeling, Computer Science (AI/ML for Life Sciences), or a related field.

• Demonstrated track record in developing and deploying AIDD or computational methods in drug discovery and molecular modeling (evidenced by high-impact publications, patents, thesis research, or pipeline implementations) in at least one of the following areas (prior experience in ADCs or bioconjugates is a strong bonus):

• Computational Linker & Small Molecule Generation: Expertise in computational linker design, scaffold hopping, de novo molecular generation, or multiparameter lead optimization (experience in ADC linkers or bioconjugates is a plus).

• AI Antibody Design & Engineering for PK/PD: Expertise in AI/computational antibody/protein design, binder optimization, or protein engineering targeted at optimizing biophysical and PK/PD properties (e.g., stability, clearance, Fc/FcRn engineering) (experience in ADCs or therapeutic antibodies is a plus).

• Small Molecule ADMET Prediction: Strong track record in developing machine learning/deep learning models for small molecule ADMET property prediction, clearance, or quantitative structure-activity/property relationships (QSAR/QSPR) (experience in ADC payloads/linkers or PK/PD modeling is a plus).

Technical & Engineering Skills

• Proficient in Python, with extensive experience using cheminformatics/structural biology libraries (e.g., RDKit, OpenBabel, OpenMM, PyMOL).

• Expertise in deep learning frameworks (PyTorch, TensorFlow) and relevant architectures (GNNs, Generative Models, Diffusion Models, Transformers) applied to molecular or ADME/PK tasks.

• Strong software engineering practices with the ability to lead the design, implementation, and maintenance of robust AI platforms.

• Familiarity with CADD tools (molecular docking, MD simulations) or PK/PD modeling tools (e.g., Simcyp, PK-Sim, NONMEM, Julia/SciML) is a plus.

Soft Skills

• Fast Learner: Ability to quickly absorb experimental assay nuances, track cutting-edge ADC industry advancements, and understand translational drug workflows.

• Independent Research: Strong capability to independently tackle complex, multidisciplinary R&D challenges at the intersection of chemistry, biology, and data science.

• Communication: Excellent cross-disciplinary communication skills, capable of translating algorithmic and ML models into actionable chemical, DMPK, and biological insights.

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Preferred Qualifications

• Open to fresh Ph.D. graduates with exemplary doctoral publications/thesis OR candidates with 1–3+ years of biotech/pharma industry or post-doc experience directly supporting ADC or bioconjugate drug discovery pipelines.

• Demonstrated experience in building computational PK/PD or machine learning models for ADME/PK prediction in small molecules or biologics.

• Experience in developing integrated AI tool platforms for drug discovery teams.