Associate Director, Physics-Based Drug Discovery Scientist
Odyssey Therapeutics is propelling drug development beyond what is now possible to deliver medicines that address critical needs of patients with cancer and inflammatory diseases. We achieve unprecedented speed and efficiency by bringing together a target-centric approach, a toolbox of cutting-edge technologies, and a team of accomplished, world-class drug hunters. By reimagining the drug development process, we are creating a deep and broad pipeline that holds the potential to transform human health.
- Job Title: Associate Director, Physics-Based Drug Discovery Scientist
- Location: Boston, Ann Arbor, or Frankfurt
- Employment Type: Full-Time
- Department: Data Sciences
In partnership with Data Science team members, you will work at the nexus of physics-based simulation and machine-learning enabled methods. You will help us to continuously improve our computational end-to-end platform including (cryptic) binding site detection, initial ligand discovery, ligand optimization, and the interrogation of drivers of potency and selectivity. You will leverage your findings into better in-silico predictions that are fed into our generative design platform. You will be part of a company where computation is a central element in program advancement.
Are you a self-motivated learner and enjoy solving problems? Do you want to help build something transformational and revolutionary? Join us and help revolutionize the application of physics-based machine-learning for patient benefit!
- Use advanced biophysical simulation (e.g., atomistic, coarse-grained methods, FEP, replica-exchange) to advance drug discovery on difficult-to-drug targets
- Use enhanced molecular simulations and free energy calculations to characterize protein-ligand association/dissociation kinetic processes
- Develop and apply new approaches for biasing molecular dynamics using machine-learning and collective variable discovery
- Use statistical modeling of high-dimensional, large-scale time series molecular simulation datasets
- Identify and improve open-source solutions for binding free energy calculations, making them readily usable for large-scale applications across the company
- Collaborate closely with our ML scientists to combine AI and physics-based simulations to achieve the accuracy of computationally expensive methods at the speed of ML algorithms
- Enrich your expertise of contemporary computational chemistry methods via collaborations, both internal and external
- Share your expertise by managing and mentoring junior team members
- Doctorate in Chemistry, Chemical Physics, Computational Chemistry, or related field
- At least three years, preferably more than five years, of work experience in an industrial setting
- A proven track record of computational method development (from idea conception to production, with demonstrable impact on the advancement of a drug discovery or technology project)
- Programming expertise in at least one programming language, preferably python
- Profound experience in molecular dynamics simulations/enhanced sampling/free energy calculation methods
- Ability to independently design, develop, and execute research assignments
- Ability to work on multiple projects in parallel
- Strong collaborative, coaching, and teaching skills
- Passion for drug discovery
- A working knowledge of medicinal chemistry and drug discovery
- Hands-on experience with ML applications
- Experience in absolute binding free energy predictions
- Familiarity with software development best practices
- Proficiency with high performance computing environments
- Experience with cloud computing (AWS)
- Strong publication record
- Experience in Markov-chain-based statistical sampling algorithms with sparse experimental data to guide iterative unbiased molecular simulations.
- Expertise in enhanced sampling simulation science with particular emphasis on variational autoencoders and reinforcement learning approaches to guide reaction coordinate sampling
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