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Research Advisor, Computational Chemistry
Location
United States
Posted
4 hours ago
Salary
0
Seniority
Senior
Job Description
Research Advisor, Computational Chemistry
Xenon Seven
• Perform molecular dynamics simulations of chemically modified oligonucleotide duplexes and single‑stranded species to characterize the structural and thermodynamic consequences of sugar, backbone, and base modifications • Apply free energy methods (FEP, thermodynamic integration, MM/PBSA, MM/GBSA) to predict modification‑dependent binding affinities, duplex stability, and protein–oligonucleotide interactions • Develop and validate force field parameters for novel nucleotide analogs using quantum mechanical calculations, enabling rapid computational evaluation of new chemistries emerging from the medicinal chemistry team • Build and apply cheminformatics descriptors and QSAR/QSPR models adapted for chemically modified oligonucleotides, moving beyond sequence‑only representations to capture the full chemical diversity of the modification space • Collaborate with medicinal chemists and biologists to integrate computational predictions with experimental SAR data, contributing to the identification of optimal modification patterns for on‑target potency, selectivity, metabolic stability, and safety • Contribute to reusable computational workflows, data assets, and modeling platforms that support cross‑program learning and integration with the team’s unified machine learning models • Present findings to cross‑functional teams and contribute to scientific strategy discussions, publications, and patent applications
Job 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 (RDKit, OpenEye, or equivalent) and/or commercial CADD platforms (Schrödinger, MOE)
- Strong programming skills in Python, with experience in scientific computing libraries
- Familiarity with machine learning and AI methods applied to molecular sciences, including experience with predictive modeling for molecular properties, chemical optimization, or structure–activity relationships
- Excellent written and oral communication skills with ability to present complex computational results to diverse scientific audiences including medicinal chemists and biologists
- Experience with high‑performance computing and/or cloud‑based simulation environments
- Demonstrated ability to work collaboratively in cross‑functional team environments
- Experience with force field parameterization for non‑standard nucleotide analogs, including QM‑derived charge fitting (RESP, AM1‑BCC) and torsion parameter development
- Familiarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of modified nucleotides and their impact on duplex stability and reactivity
- Understanding of how chemical modifications influence oligonucleotide secondary structure, folding, and conformational dynamics, including modification‑dependent effects on duplex geometry and protein recognition
- Experience with machine learning approaches for molecular property prediction, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer data
- Familiarity with molecular representations for modified oligonucleotides (HELM, extended SMILES, or similar macromolecular encoding schemes)
- 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 demonstrating expertise in computational chemistry applied to nucleic acids or modified biopolymers
- Deep understanding of nucleic acid structure and chemistry, including familiarity with common therapeutic modifications (2’‑OMe, 2’‑F, 2’‑MOE, LNA/cET, phosphorothioate, GalNAc conjugates)
- Experience designing computational workflows that integrate with automated experimental platforms and high‑throughput screening
- Proficiency in Rust or other systems‑level languages for performance‑critical scientific computing is a plus
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