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 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