Research Advisor, Computational Chemistry

This is a fully remote position, open to applicants in United States.

📋 Description

• Conduct molecular dynamics simulations of chemically altered oligonucleotide duplexes and single-stranded species to analyze the structural and thermodynamic implications of modifications to sugar, backbone, and bases.

• Utilize free energy techniques (FEP, thermodynamic integration, MM/PBSA, MM/GBSA) to forecast binding affinities, duplex stability, and interactions between proteins and oligonucleotides that depend on modifications.

• Create and validate force field parameters for novel nucleotide analogs through quantum mechanical calculations, facilitating swift computational assessments of new chemistries developed by the medicinal chemistry team.

• Construct and implement cheminformatics descriptors and QSAR/QSPR models tailored for chemically modified oligonucleotides, advancing beyond mere sequence representations to encompass the complete chemical diversity of the modification landscape.

• Collaborate with medicinal chemists and biologists to merge computational predictions with experimental SAR data, aiding in the determination of optimal modification patterns for on-target potency, selectivity, metabolic stability, and safety.

• Contribute to the development of reusable computational workflows, data assets, and modeling platforms that enhance cross-program learning and align with the team’s integrated machine learning models.

• Share findings with cross-functional teams and participate in discussions on scientific strategy, publications, and patent submissions.

⛳️ Requirements

• PhD in computational chemistry, physical chemistry, chemical physics, biophysics, or a related discipline.

• Proven expertise in molecular dynamics simulation of nucleic acids or chemically modified biopolymers.

• Familiarity with free energy calculation techniques applied to biomolecular systems.

• Proficient in cheminformatics toolkits (RDKit, OpenEye, or similar) and/or commercial CADD platforms (Schrödinger, MOE).

• Strong programming capabilities in Python, including experience with scientific computing libraries.

• Knowledge of machine learning and AI applications in molecular sciences, particularly in predictive modeling for molecular properties, chemical optimization, or structure–activity relationships.

• Exceptional written and verbal communication skills with the ability to convey complex computational results to varied scientific audiences, including medicinal chemists and biologists.

• Experience with high-performance computing and/or cloud-based simulation environments.

• Demonstrated ability to collaborate effectively in cross-functional team settings.

• Experience in 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 analyzing the electronic structure of modified nucleotides and their effects on duplex stability and reactivity.

• Understanding of how chemical modifications affect oligonucleotide secondary structure, folding, and conformational dynamics, including modification-dependent influences on duplex geometry and protein recognition.

• Experience with machine learning techniques for predicting molecular properties, including graph neural networks, molecular language models, or transformer-based architectures applied to chemical or biopolymer datasets.

• Knowledge of molecular representations for modified oligonucleotides (HELM, extended SMILES, or similar macromolecular encoding formats).

• Understanding of oligonucleotide-specific ADME characteristics, such as nuclease-mediated metabolism, plasma protein binding of phosphorothioate backbones, and endosomal escape mechanisms.

• A solid record of peer-reviewed publications showcasing expertise in computational chemistry related to nucleic acids or modified biopolymers.

• Comprehensive 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 in designing computational workflows that integrate with automated experimental platforms and high-throughput screening processes.

• Proficiency in Rust or other systems-level programming languages for performance-critical scientific computing is advantageous.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health and wellness benefits.

• Opportunities for professional development and continuing education.

• Flexible working arrangements including remote work options.

• Collaborative and innovative work environment.