Computational Medicinal Chemist (In Silico Design)(J24813)

We are looking for a hands on Computational Medicinal Chemist to join our integrated discovery team in Shanghai. You will use state of the art AI and CADD technologies to design, prioritize and optimize novel small molecules for global biotech clients. This is a client facing, delivery oriented role: you will run end to end in silico design campaigns, participate in live design sessions with medicinal chemists and clients, and drive compounds into hit to lead and lead optimization workflows that feed experimental validation. Key responsibilities • Lead structure based and ligand based drug design campaigns using computational and AI tools to generate and optimize novel small molecule ideas. • Design and execute virtual screening, docking, free energy perturbation and multi parameter optimization (MPO) workflows to prioritize compounds for synthesis and testing. • Translate biological and structural data into actionable hypotheses and SAR strategies; propose focused libraries and bespoke molecule designs. • Run interactive “live design” sessions with internal chemists and external clients to refine ideas in real time and accelerate decision making. • Build and maintain reproducible computational workflows and scripts (automation, data management) to support multiple projects concurrently. • Collaborate closely with synthetic chemists, DMPK, and assay groups to interpret data, update models and iterate design cycles. • Contribute to client reports, presentations and scientific proposals; present computational results clearly to non computational stakeholders. • Mentor junior computational scientists and help grow best practices for CADD/AI integration across the company

• Ph.D. in Computational Chemistry, Medicinal Chemistry, Organic Chemistry or a closely related discipline. • 5+ years' industry experience in biopharma discovery, ideally within discovery teams at pharma or top tier integrated CROs. • Deep, demonstrable experience in SBDD and LBDD workflows, virtual screening, MPO and hit to lead progression. • Proficiency with core tools: Schrödinger Suite (Glide, FEP+), OpenEye, MOE, Cresset; comfortable scripting and integrating these tools into pipelines. • Strong Python skills for workflow automation and data analysis; familiarity with RDKit advantageous. • Excellent communication skills; proven experience in client facing situations and presenting complex computational results to mixed audiences. • Comfortable leading interactive “live design” sessions and making rapid, high quality design decisions. Preferred / Nice-to-have • Co authored patents or publications linked to pre clinical or clinical candidates. • Prior experience at top integrated CROs or Big Pharma discovery teams. • Experience applying machine learning or generative models to small molecule design integrated with physics based methods. • Track record of translating computational designs into syntheses that produced experimental hits.