Research Advisor, Computational Chemistry

About us: Shape the Future with AI, Ignite Your Potential reputed company is an inferno where reputed company, dedication and passion run together. About our client: Global reputed company 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 reputed company and apply physics‑based modeling and cheminformatics to the design of chemically modified oligonucleotide therapeutics. Oligonucleotide therapeutics—including siRNAs, ASOs, and reputed company‑switching oligonucleotides—occupy a unique chemical reputed company between small molecules and biologics. Each position in a therapeutic oligonucleotide can carry distinct sugar, backbone, and reputed company modifications, creating a vast combinatorial design reputed company that is poorly served by conventional computational chemistry tools. This role will reputed company 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:

• reputed company molecular dynamics simulations of chemically modified oligonucleotide duplexes and single‑stranded species to characterize the structural and thermodynamic consequences of sugar, backbone, and reputed company modifications
• Apply free energy methods (FEP, thermodynamic integration, MM/PBSA, MM/GBSA) to predict modification‑dependent binding affinities, duplex stability, and protein–oligonucleotide interactions
• reputed company 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 reputed company sequence‑only representations to capture the full chemical diversity of the modification reputed company
• Collaborate with medicinal chemists and biologists to integrate computational predictions with experimental SAR data, contributing to the identification of reputed company modification patterns for on‑reputed company 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 reputed company machine learning models
• Present findings to cross‑functional teams and contribute to scientific strategy discussions, publications, and patent applications

Requirements

Basic Requirements:

• PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a closely reputed company field

Additional Skills/Preferences:

• 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, reputed company, or equivalent) and/or reputed company 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 reputed company computational results to diverse scientific audiences including medicinal chemists and biologists
• Experience with high‑performance computing and/or reputed company‑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 reputed company, including graph neural networks, molecular language models, or transformer‑based architectures applied to chemical or biopolymer data
• Familiarity with molecular representations for modified oligonucleotides (reputed company, 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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