Experteer Overview In this role you apply advanced in silico methods to design and optimize synthetic therapeutic molecules, shaping early drug discovery efforts. You will collaborate with chemists, biologists and DMPK teams to translate computational outputs into experiments and decisions, contributing to programs in Immunology, Neuroscience and Oncology. You’ll help advance new computational workflows and technologies to increase efficiency in discovery. This position offers global collaboration, leadership within the ISD, and a pathway to impacting patient outcomes through innovative medicines.Compensaciones / Beneficios• Lead design and optimization of synthetic molecules by integrating structural data, simulations, ML/generative models, and assay results to inform hypotheses and timelines• Develop and apply predictive models for SAR, on/off-target activity, physicochemical properties, PK/PD, and synthetic feasibility to influence compound progression• Collaborate with medicinal chemistry, biology and DMPK to align computational outputs with experimental plans• Work with computational technology and platform teams to prioritize and validate new computational workflows and technologies• Represent ISD in governance forums and external collaborations, communicating impact of computational work to team members and leadershipResponsabilidades• Ph.D. with postdoctoral training in computational chemistry or related field• 6–12 years of pharma/biotech industry experience with computational modeling contributing to compound progression and discovery projects• Strong understanding of the drug discovery process• Broad knowledge of in silico methods including physics-based and AI/ML approaches for drug discovery applications• Experience with emerging modalities (synthetic peptides, PROTACs, induced proximity modalities) is preferred• Experience with CADD across multiple targets and therapeutic areas; strong SBDD background• Experience with free energy/MD methods (e.g., FEP), cheminformatics, data science and ML frameworks (e.g., scikit-learn, pytorch)• Scientific programming skills; familiarity with KNIME and Python• Mentoring, cross-functional collaboration, and integral teamwork experience• Familiarity with tools such as Maestro/Schrodinger, MOE, OpenEye; scripting for workflows in KNIME and Python notebooks; LiveDesign & Spotfire as preferredRequisitos principales•