At Roivant, we are passionate about discovering and developing new drugs to impact patients' lives. Since its inception in 2014, Roivant has launched over 20 portfolio companies (Vants), overseen 5 successful IPOs, established a $3B partnership with a global pharma, built a pipeline of over 40 assets across various modalities and therapeutic areas, and delivered 8 successful phase 3 readouts.
Roivant is currently building new capabilities in drug discovery and expanding its existing development engine to become the world's leading tech-enabled pharmaceutical company. Roivant's drug discovery capabilities are driven by our computational discovery platform, which combines preeminent physics-based tools with deep expertise in machine learning to generate unprecedented predictive power that can tackle previously intractable discovery challenges. The tight integration of this computational platform with our experimental capabilities enables the rapid design and optimization of new drugs to address a wide range of targets for diseases with high unmet need.
We believe that the future of drug discovery lies in integrating predictive sciences, biology, and medicinal chemistry to accelerate the path to new medicines. This role is an opportunity to be an architect of this paradigm shift and generate transformative benefit for patients.
Roivant Discovery is looking for a senior research scientist in computational structural biology to join our computational platform team. Working closely with other researchers in our drug discovery organization, the candidate will lead the application—and inform the development—of the state-of-the-art computational methods to model protein structures and dynamics to discover and enable new therapeutic targets.
- Use modeling and simulations to generate actionable insight for target evaluation and drug discovery
- Build high-quality structural models of proteins and protein-ligand complexes based on experimental data (including coevolutionary data), homology, molecular simulations and machine-learning models
- Collaborate with the biophysics and simulation teams to investigate the structural and dynamic basis of protein functions
- Collaborate with bioinformatics and genomics researchers to develop computational tools for evaluating potential drug targets
- Work with the platform team to develop and evaluate new methods and models for simulating protein structures and dynamics
- M.S. or Ph.D. in chemistry, biology, or related fields
- Research experience in structural biology, having built structural models of proteins from X-ray crystallography or NMR.
- Good knowledge of other common experimental techniques in structural characterization of proteins, such as SAXS, FRET, HDX, etc..
- Experience with computational modeling of proteins and protein-ligand complexes, e.g., molecular dynamics simulations, docking, homology modeling, etc..
- Familiar with one or more software tools in structural biology, such as O, CCP4, Phenix, Rosetta, Modeller, Pymol, VMD, AMBER, etc..
- Strong communication skills
Additional Desirable Qualifications:
- Research experience in structure-based drug design
- Proficient in one or more programming languages (Python preferred)
- Experience working with an interdisciplinary team to solve a complex scientific problem.
Roivant Sciences provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.