Senior Principal Scientist, CADD

Overview At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at Job Function Data Analytics & Computational Sciences Job Sub Function Data Science Job Category Scientific/Technology All Job Posting Locations Beerse, Antwerp, Belgium Job Description Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at Johnson & Johnson Innovative Medicine (J&J IM) is recruiting for a Senior Principal Scientist, Computer-Aided Drug Design (CADD) - In Silico Discovery to join our Therapeutics Discovery team located in Beerse (Belgium). The Therapeutics Discovery organization within J&J IM is continuing to build scientific expertise in modeling, screening, pharmacology and chemistry to partner closely with therapeutic area scientists to develop groundbreaking new medicines in the areas of Immunology, Neuroscience and Oncology. The In Silico Discovery (ISD) department in Therapeutics Discovery is seeking a highly experienced scientist to bring their expertise to exciting and novel areas of computational drug design. Key Responsibilities Lead the design and optimization of synthetic therapeutic molecules by integrating structural data, simulations, ML/generative models, and assay results to help pose hypotheses and address project challenges and timelines. Apply and develop predictive models to gain SAR insights, on- and off-target activity, physicochemical properties, PK/PD, and synthetic feasibility to impact compound progression. Collaborate with medicinal chemists, biologists and DMPK scientists to ensure computational outputs directly inform experimental plans and progression decisions. Collaborate with computational technology development and platform teams to help prioritize, validate and improve emerging computational technologies and novel workflows to ultimately impact molecular design and efficiencies in discovery portfolio programs. Represent ISD in internal governance forums and external collaborations; evaluate and effectively communicate the impact of computational work on their own project to other team members as well as senior leadership. Qualifications A Ph.D. and postdoctoral training in computational chemistry or a related field, followed by at least 6-12 years of experience applying computational modeling in a pharmaceutical/biotech industrial setting with a track record of key contributions to compound progression and invention in discovery projects is required. Strong understanding of drug discovery process is required. Broad understanding & critical, real-world views into a broad arsenal of in silico approaches & tools including physics-based and AI/ML for drug discovery project applications is strongly preferred. Experience with emerging therapeutic modalities such as synthetic peptides & targeted protein degraders/induced proximity modalities (PROTACs, RIPTACs and molecular glues) is strongly preferred. Experience with CADD applications in multiple therapeutic areas and target classes is strongly preferred. A strong background in structure-based drug design (SBDD) is required. Expertise in one or several of the following areas is strongly preferred: free energy/ MD approaches (e.g. FEP), cheminformatics toolbox applications, data science and ML frameworks (e.g. scikit-learn, pytorch), scientific programming, etc. Experience working in a cross-functional environment, mentoring scientists, influencing decisions, integrating multiple disciplines, and resolving conflicts is required. Experience working with global teams is strongly preferred. Familiarity with multiple commercial computational drug discovery packages (examples include, Maestro/Schrodinger, MOE/Chemical Computing Group, and/or OpenEye tools) is required Experience scripting computational workflows for project specific problems in KNIME and Python notebooks is preferred. Experience using LiveDesign & Spotfire for design and analysis or related platforms is strongly preferred. Strong record of creative scientific contributions including peer reviewed publications, patent applications, and presentations at major conferences is preferred. Located in Beerse, Belgium and requires up to 10% travel internationally. Hybrid role. Required Skills Preferred Skills: Computer Aided Drug Design #J-18808-Ljbffr