Scientist II, Computational Biology
3 weeks ago
Responsibilities:
- Develop and deploy computational tools for prognosticating protein-protein interaction potential and identifying prospective proteins for examination.
- Devise highly precise scoring mechanisms for protein-ligand interactions, encompassing methodologies like liberated energy perturbation (LEP), thermodynamic integration (TI), and artificial intelligence/machine learning techniques.
- Set up a high-performance computing (HPC) cloud infrastructure for expedited computational drug design employing docking, molecular dynamics, and liberated energy strategies.
- Employ MD or pertinent methodologies to anticipate sites amenable to ligands.
- Work closely with pharmaceutical chemists to transform computational forecasts into ligand blueprints and furnish counsel for compound synthesis and assessment.
- Conduct virtual screening and molecular modeling inquiries to discern and prioritize plausible protein targets for disintegration.
- Scrutinize and construe experimental data, encompassing binding affinities and protein degradation data, to steer compound enhancement.
- Forge and maintain computational chemistry and biology utilities and workflows to refine and accelerate protein modeling, virtual screening, and compound design processes.
- Stay abreast of the latest advances in molecular dynamics, protein degradation, and pharmaceutical discovery domains, and actively incorporate novel methodologies to enhance project outcomes.
- Contribute to scholarly publications, patents, and presentations to disseminate research discoveries and enrich the company's intellectual property portfolio.
- Collaborate effectively within interdisciplinary teams, offering computational insights and expertise to advance project objectives and deadlines.
Required Qualifications:
- Ph.D. in Computational Chemistry, Computational Biology, Computational Biophysics, or related discipline with 4+ years of industry experience
- Thorough comprehension of molecular dynamics, heightened sampling methodologies, liberated energy strategies, and their applicability to drug design.
- Background in prognosticating protein-protein interactions computationally.
- Proficiency in executing molecular dynamics on protein-ligand complexes and parsing trajectories to investigate critical interaction components.
- Proficiency in molecular dynamics software like AMBER, GROMACS, and NAMD.
- Strong programming prowess in contemporary scientific languages (Python/C++/etc.).
- Experience with Python APIs for scrutinizing protein dynamics and protein-ligand interaction, e.g., MDTraj, MDAnalysis, and Schrodinger Python API.
- Mastery of Linux OS and command-line operations.
- Experience in distributed computing leveraging HPC clusters or cloud infrastructures.
- Familiarity with commercial modeling tools such as Schrodinger and OpenEye.
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