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 chemical physicists/physical chemists to join our computational platform team. Working closely with other platform team members, the candidate will implement computational models in molecular physics to enable computation-driven drug discovery. Competitive pay, equity, strong perks, and a fun working environment, along with the opportunity to do cutting edge science to design better medicines, are all good reasons to join us!
- Develop and implement models and methods in computational molecular physics, including but not limited to
- Develop accurate and computationally efficient models of molecular interactions, including quantum chemistry, force field, and machine-learning models, for small molecules, proteins, and nucleic acids
- Develop multiscale simulation methods that combine force field, quantum chemistry, and machine learning models to enable highly accurate molecular simulations, including binding free energy predictions and protein conformational ensemble predictions
- Leverage existing biophysical data and collaborate with experimental groups to design experiments and obtain new data to validate the models and simulation methods
- Develop robust protocols for parameterizing classical force fields and validate such protocols against a wide range of experimental data
- Collaborate with platform teams to deploy the above models and simulation methods in target evaluation and drug discovery projects to enable or substantially accelerate such efforts
- Work with experimental groups to validate and benchmark the computational models in drug discovery projects
- Highly motivated to develop computational methods for discovering better medicines
- M.S. or Ph.D. in computational physics/chemistry, physical chemistry/chemical physics, applied mathematics, or related fields
- Strong record of past research accomplishments
- Extensive past experience in quantum chemistry, force field development, and molecular dynamics simulations
- Extensive programming experience (C/C++ and Python preferred)
- Excellent communication skills and strong team player
Additional Desirable Qualifications:
- Experience working with a diverse team on an ambitious project
- Experience in deep learning and numerical optimization