Description

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