Location: Boston, MA
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 analytical 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.
Silicon Therapeutics is looking for a highly motivated Research Scientist with experience in molecular dynamics (MD) simulations of protein complexes and applications of machine learning (ML) for simulation analysis. She/he will be responsible for exploring conformational dynamics of protein-protein complexes to predict binding and signaling. This position will involve methodological developments to push the boundaries of what is possible using a large distributed computational infrastructure of GPUs and CPUs. Python programming and simulation analysis experience is required, with an emphasis on advanced simulation techniques such as Umbrella Sampling, Markov State Modeling, or Weighted Ensemble. Experience with machine learning of proteins and small molecules is a plus.
• Design, run, and analyze advanced simulations to explore the conformational dynamics of protein-ligand and protein-protein interactions.
• Perform and analyze simulations at scale (hundreds to thousands of simulations).
• Work closely with experimental scientists to incorporate structural and dynamical data into simulations, and to use data for simulation validation.
• Work closely with Silicon Tx scientists in our drug discovery efforts.
• Investigate latest simulation and experimental trends and propose adoption where appropriate.
• Work closely with Silicon Tx research programmers to implement and test new methods.
• Ph.D. in Computational Chemistry, Computational Biology, Biophysics, Computer Science, or a related discipline, with a specific focus on applications of molecular dynamics simulations.
• Extensive knowledge of, and experience with, molecular dynamics simulations of protein-ligand or protein-protein systems.
• Demonstrated ability to program in Python.
• Strong interpersonal, communication, and teamwork skills.
• Ability to conduct independent research.
Additional Preferred Qualifications
• Experience applying machine learning to structural questions for proteins and ligands.
• Experience with advanced simulation methods for path sampling, such as milestoning, Weighted Ensemble, metadynamics, and Markov State Models.
• Experience with free energy and binding kinetics calculations.
• Desire for leadership and to mentor other investigators.
• Industrial experience using simulations in a drug discovery setting.
• Strong publication record.
Silicon Therapeutics 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.