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 https://www.jnj.com **Job Function:** Discovery & Pre-Clinical/Clinical Development **Job Sub** **Function:** Chemical Research **Job Category:** Scientific/Technology **All Job Posting Locations:** Beerse, Antwerp, Belgium, Toledo, Spain **Job Description:** We have two entry-level positions for talented computational Senior Scientists, Computer Aided Drug Design joining our cutting edge In Silico Discovery department within Therapeutics Discovery at J&J. The positions require using computational methods in areas such as structure-based drug design, physics-based modeling, machine learning, co-folding and more, to identify hits, design molecules and drive our early drug discovery projects. A particular focus for these positions is to contribute to virtual screening (VS), physics-based methods (e.g. FEP) especially for VS, co-folding structure prediction and the increased overlap and use of these methods. We are looking for people with teamwork and communication skills to engage in a matrix environment and across multi-disciplinary portfolio and technology teams. Responsibilities of the Senior Scientists will include: + Explore and develop workflows that lead to improvements for virtual screening and early hit-finding efforts. Assess and recommend best practices on for instance the use of free energy methods or co-folding for VS + Work with others to explore and develop best practices for the use of co-folding in many different computational workflows + In the long term, and with the support of colleagues, identify emerging technologies and methods in structure-based, physics-based, or 3D AIML fields and test and introduce them into In Silico Discovery at J&J + Work with the broader VS team to contribute to the databases, evaluating methods, best practice workflows, VS informatics platform and improving success rates + Impact drug discovery projects by developing and executing clear computational strategies and workflows to design complex molecules and predict their properties using structure-based and machine learning methodologies + Contribute your recommendations to drug discovery project teams consisting of medicinal chemists, disease biologists, structural biologists, etc. + Play an important role in external collaborations and consortia in the key areas mentioned: physics-based, VS, co-folding etc. Integrate collaboration deliverables internally + In general, work positively and collaboratively across In Silico Discovery with our global CADD, Drug Discovery Data Sciences, Cheminformatics and In Silico Biologics teams. + Ensure optimal interaction/communication and provide updates on project status to discovery leadership **Required:** + A PhD in computational chemistry or a related field + Good knowledge of chemistry and protein structure + Experience working in 3D protein-ligand computational modeling, whether AIML or physics-based + Strong problem-solving skills for developing creative, innovative solutions, and meeting project objectives are required + Experience with HPC and general compute and job management + Strong communication and teamworking skills + Track record of scientific deliveries, including peer reviewed first-author publications and presentations at major national meetings is required + Experience coding and developing workflows in Python + A basic understanding of medicinal chemistry principals and concepts that are applied in drug discovery + Up to 10% travel both domestically and internationally is required **Preferred skills:** + Computational chemistry Postdoctoral studies, limited industry experience or exposure to drug discovery is beneficial. + Deeper multi-year exposure to areas such as free energy methods, or 3D AIML affinity or structure prediction + Experience with open-source resources, libraries and toolkits such as ChEMBL, RDKit, etc + Experience with modern AIML libraries and platforms + Solid understanding of generative design + Experience with commercial software packages for molecular modeling: Maestro (Schrodinger), Openeye, MOE (Chemical computing group) + Experience with large chemical spaces used in virtual screening + Experience with existing virtual screening methodologies such as docking, and ligand-based methods