Research Advisor, Computational Chemistry

Job not on LinkedIn

🔥 0 minutes ago

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Xenon Seven

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🏢 Enterprise

Artificial Intelligence • Enterprise

Xenon Seven is a company that specializes in providing AI and data solutions across various industries, including healthcare, financial services, consumer retail, high-tech, automotive, and energy utilities. With expertise in generative AI, decision support systems, predictive analytics, computer vision, robotics, and mechatronics, they support businesses in AI preparedness, exploration, transformation, and scalability. Xenon Seven works with a network of AI technologists and experts from over 20 institutions, focusing on solving complex problems and ensuring secure and seamless AI transitions for businesses. Their services aim to optimize AI infrastructure and performance while maintaining strict security standards.

📋 Description

• 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

🎯 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