Research Advisor, Computational Chemistry

About us:Shape the Future with AI, Ignite Your PotentialXenon7 is an inferno where skill, dedication and passion run together.About our client:Global healthcare leader headquartered in Indianapolis, Indiana. The Cardiometabolic Research (CMR) Therapeutic Area of our client, focuses on the discovery of biologic, small molecule and genetic therapeutics for the treatment of cardiometabolic diseases and associated complications. We are seeking a highly motivated computational chemist to join our team and apply physics‑based modeling and cheminformatics to the design of chemically modified oligonucleotide therapeutics.Oligonucleotide therapeutics—including siRNAs, ASOs, and splice‑switching oligonucleotides—occupy a unique chemical space between small molecules and biologics. Each position in a therapeutic oligonucleotide can carry distinct sugar, backbone, and base modifications, creating a vast combinatorial design space that is poorly served by conventional computational chemistry tools. This role will bridge molecular simulation, cheminformatics, and machine learning to generate actionable insights that guide the optimization of chemically modified oligonucleotides across our client's RNA therapeutics portfolio.Key responsibilities include: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 modificationsApply free energy methods (FEP, thermodynamic integration, MM/PBSA, MM/GBSA) to predict modification‑dependent binding affinities, duplex stability, and protein–oligonucleotide interactionsDevelop 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 teamBuild 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 spaceCollaborate 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 safetyContribute to reusable computational workflows, data assets, and modeling platforms that support cross‑program learning and integration with the team’s unified machine learning modelsPresent findings to cross‑functional teams and contribute to scientific strategy discussions, publications, and patent applicationsRequirementsBasic Requirements:PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a closely related fieldAdditional Skills/Preferences:Demonstrated expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymersExperience with free energy calculation methods applied to biomolecular systemsProficiency in cheminformatics toolkits (RDKit, OpenEye, or equivalent) and/or commercial CADD platforms (Schrödinger, MOE)Strong programming skills in Python, with experience in scientific computing librariesFamiliarity with machine learning and AI methods applied to molecular sciences, including experience with predictive modeling for molecular properties, chemical optimization, or structure–activity relationshipsExcellent written and oral communication skills with ability to present complex computational results to diverse scientific audiences including medicinal chemists and biologistsExperience with high‑performance computing and/or cloud‑based simulation environmentsDemonstrated ability to work collaboratively in cross‑functional team environmentsExperience with force field parameterization for non‑standard nucleotide analogs, including QM‑derived charge fitting (RESP, AM1‑BCC) and torsion parameter developmentFamiliarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of modified nucleotides and their impact on duplex stability and reactivityUnderstanding of how chemical modifications influence oligonucleotide secondary structure, folding, and conformational dynamics, including modification‑dependent effects on duplex geometry and protein recognitionExperience with machine learning approaches for molecular property prediction, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer dataFamiliarity 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 escapeTrack record of peer‑reviewed publications demonstrating expertise in computational chemistry applied to nucleic acids or modified biopolymersDe