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About the Role
We are seeking a Computational Chemist to help generate novel molecules for a variety of targets and collaborate closely with AI researchers and experimental scientists.
Responsibilities
- Develop scalable workflows to generate and screen thousands of potential drug candidates.
- Design and run molecular dynamics simulations, applying enhanced sampling methods to characterize protein-ligand binding and conformational dynamics.
- Engineer and optimize data pipelines that handle large-scale molecular simulations and structure-based predictions.
- Build and optimize cheminformatics pipelines for compound filtering, property prediction, fingerprint-based similarity search, and chemical space analysis.
- Collaborate with AI researchers to incorporate simulation data into model training and inference pipelines.
- Bridge scientific rigor with practical results, ensuring computational protocols are both accurate and efficient.
- Communicate technical nuances to cross-functional teams, fostering a shared understanding of capabilities and limitations.
- Stay up to date with the latest research and breakthroughs in computational chemistry, generative models, and molecular simulation.
- Lead external scientific presentations to pharmaceutical partners; Communicate the team's computational approaches for both technical audiences and business partners.
Requirements
- PhD in Computational Biophysics, Computational Chemistry, or a related field.
- Experience with molecular modeling, simulation, and generative drug design.
- Proficiency with common computational biophysics software (e.g. AMBER, GROMACS, OpenMM, PLUMED, Schrödinger, OpenEye).
- Strong background in cheminformatics: molecular fingerprints, QSAR/QSPR, property prediction, scaffold analysis, cheminformatics libraries (e.g., RDKit, OpenBabel).
- Expert coding in at least one language (Python preferred).
- Familiarity with HPC cluster or cloud computing environments, containerization, and workflow orchestration.
- Strong communication skills, with the ability to present complex ideas in clear terms.
- Experience integrating machine learning techniques for molecular generation.
- Background in quantum chemistry methods (DFT, ab initio) and its application to biomolecular systems.
- Exposure to early-stage drug discovery programs, from target identification to lead optimization.
Skills
- Molecular modeling
- Molecular simulation
- Generative drug design
- AMBER
- GROMACS
- OpenMM
- PLUMED
- Schrödinger
- OpenEye
- Cheminformatics
- Molecular fingerprints
- QSAR/QSPR
- Property prediction
- Scaffold analysis
- RDKit
- OpenBabel
- Python
- HPC cluster computing
- Cloud computing
- Containerization
- Workflow orchestration
- Machine learning
- Quantum chemistry methods
- DFT
- Ab initio methods
Education Level
- PhD in Computational Biophysics, Computational Chemistry, or a related field
About the Company
- At Topos Bio, we're pioneering drug discovery for intrinsically disordered proteins—targets that have eluded conventional approaches in neurodegeneration, oncology, and cardiometabolic disease.