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Research Advisor, Computational Chemistry

2 months ago
United States
Full-time
Lead
Data Scientist
Data Science and Analytics
Helm Machine Learning Python Rust
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Xenon7

Xenon7

Xenon7 provides advanced AI solutions and consultancy services, leveraging a team of highly qualified experts and a strong emphasis on research and innovation to address complex industry challenges and enhance operational efficiency.

Internet Software & Services
Founded 2014
View All Jobs 27

Description

  • Perform molecular dynamics simulations of chemically modified oligonucleotide duplexes and single-stranded species to assess structural and thermodynamic effects of modifications.
  • Apply free energy methods such as FEP, thermodynamic integration, MM/PBSA, and MM/GBSA to predict binding affinity and duplex stability changes.
  • Develop and validate force field parameters for novel nucleotide analogs using quantum mechanical calculations.
  • Build cheminformatics descriptors and QSAR/QSPR models tailored to chemically modified oligonucleotides.
  • Collaborate with medicinal chemists and biologists to combine computational predictions with experimental SAR data.
  • Help identify optimal modification patterns that improve potency, selectivity, metabolic stability, and safety.
  • Contribute reusable computational workflows, data assets, and modeling platforms for cross-program learning.
  • Integrate computational outputs with the team’s machine learning models and broader RNA therapeutics portfolio.
  • Present findings to cross-functional teams and contribute to scientific strategy, publications, and patent applications.

Requirements

  • PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a closely related field.
  • Demonstrated expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymers.
  • Experience with free energy calculation methods applied to biomolecular systems.
  • Proficiency in cheminformatics toolkits such as RDKit or OpenEye, or commercial CADD platforms such as Schrödinger or MOE.
  • Strong programming skills in Python with experience in scientific computing libraries.
  • Familiarity with machine learning and AI methods applied to molecular sciences and predictive modeling.
  • Excellent written and oral communication skills for presenting complex results to diverse scientific audiences.
  • Experience with high-performance computing and/or cloud-based simulation environments.
  • Demonstrated ability to work collaboratively in cross-functional teams.
  • Experience with force field parameterization for non-standard nucleotide analogs, including QM-derived charge fitting and torsion parameter development.
  • Familiarity with quantum chemical methods such as DFT or ab initio analysis for modified nucleotides.
  • Understanding of how chemical modifications influence oligonucleotide structure, folding, conformational dynamics, and protein recognition.
  • Experience with machine learning approaches such as graph neural networks, molecular language models, or transformer-based architectures.
  • Familiarity with molecular representations for modified oligonucleotides such as HELM or extended SMILES.
  • Knowledge of oligonucleotide-specific ADME properties, including nuclease-mediated metabolism, plasma protein binding of phosphorothioate backbones, and endosomal escape.
  • Track record of peer-reviewed publications in computational chemistry applied to nucleic acids or modified biopolymers.
  • Deep understanding of nucleic acid structure and chemistry, including common therapeutic modifications such as 2’‑OMe, 2’‑F, 2’‑MOE, LNA/cET, phosphorothioate, and GalNAc conjugates.
  • Experience designing computational workflows that integrate with automated experimental platforms and high-throughput screening.
  • Proficiency in Rust or other systems-level languages is a plus.

Interested in this position?

Apply directly on the company website

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