About the job
Cheminformatics and Machine Learning Internship
Genesis Molecular AI is assembling an elite computational team to revolutionize small molecule property predictions, leveraging cutting-edge machine learning and physics-based techniques. We are looking for emerging scientists proficient in machine learning, cheminformatics, and physics methodologies to propel our ambitious drug discovery initiatives.
About the Role
This internship presents a unique opportunity to become an integral part of our machine learning and cheminformatics team, directly influencing our internal ML and physics-driven platform that supports our drug discovery efforts.
Your responsibilities will likely include developing innovative strategies for potency and ADME/PK property predictions, as well as integrating with leading tools for molecular modeling.
Your Responsibilities
Drive a pioneering research project from concept to completion, aimed at enhancing our internal potency and ADME prediction tools.
Implement innovative techniques from recent scientific literature. Design and conduct extensive experiments to validate effective methods, utilizing both internal and public benchmarks.
Collaborate closely with our computer-aided drug discovery scientists and medicinal chemists to refine, benchmark, and implement enhancements to our drug discovery platform in relation to our internal and partnered projects.
Your Profile
A graduate student with a strong background in developing cheminformatics tools or physics methods applicable to drug discovery.
A proficient Python programmer capable of navigating and contributing to complex codebases.
An accomplished machine learning practitioner, knowledgeable about various architectures, their benefits, and limitations, with a track record of troubleshooting real-world applications.
A meticulous data scientist adept at managing diverse data sources. Familiarity with cheminformatics libraries such as RDKit and OpenEye is advantageous.
Driven by a passion to make a tangible impact on drug discovery initiatives while engaging with a diverse team of machine learning experts, medicinal chemists, and drug discovery scientists.