Role Overview We are seeking a highly motivated Ph.D. expert with a profound scientific background in computational chemistry/biology and exceptional AI/ML skills to join our AI Drug Discovery (AIDD) team. The successful candidate will focus on advancing computational and AI-driven approaches for Antibody-Drug Conjugates (ADCs), specifically leading linker design, pharmacokinetic (PK) prediction and multi-parameters optimization. In this role, you will work closely with cross-functional teams (Biologics, Medicinal Chemistry and DMPK) to accelerate the discovery of differentiated ADC candidates. ________________________________________ Key Responsibilities 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. ________________________________________ Qualifications 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. ________________________________________ 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.