Genesis Molecular AI · San Mateo, CA
Skills: Python
Computational Chemistry Intern, Summer 2027 Genesis Molecular AI is bringing together a world-class computational team to build the industry’s fastest and most accurate small molecule property predictions by combining machine learning and physics-based methods. We are looking for early-career scientists who develop and apply computational chemistry and physics methods, increasingly with AI in the loop, to push forward our challenging drug discovery programs. You will - Work with a dedicated mentor on a focused research project that matches your interests and expertise and addresses a key challenge in our internal and partnered drug discovery programs. - Develop and refine computational chemistry and physics methods. Potential project areas include quantum chemistry (DFT and wavefunction methods, QM/MM, torsional and conformational energetics, protonation and tautomer states), free energy simulations (relative and absolute binding free energies), and enhanced sampling (metadynamics, replica exchange, adaptive sampling). - Collaborate with our machine learning team on molecular modeling and property prediction. Potential projects include machine-learned interatomic potentials (MLIPs) and hybrid ML/MM simulations, learned molecular representations, learned collective variables for enhanced sampling, generative models of conformational ensembles, protein–ligand co-folding and binding affinity prediction, and ADME property prediction. - Work closely with our computer-aided drug discovery scientists and medicinal chemists to benchmark methods on real program data, test predictions prospectively, and integrate successful improvements into our drug discovery platform. You are - A PhD student with a proven track record of applying or developing computational chemistry and physics methods. Exceptional MS or advanced undergraduate students with substantial research experience are also encouraged to apply. - Experienced in one or more areas of computational chemistry: quantum c…
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