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.