Topos Bio
At Topos Bio, we're pioneering drug discovery for intrinsically disordered proteins—targets that have eluded conventional approaches in neurodegeneration, oncology, and cardiometabolic disease.
We’re seeking a Computational Chemist to help generate novel molecules for a variety of targets and collaborate closely with AI researchers and experimental scientists.
What you will do
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
What we are looking for
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
Preferred
Experience integrating AI or machine learning techniques for molecular generation (e.g., generative adversarial networks, reinforcement learning)
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