The Position
2023 Summer Intern - Computational Chemistry
Department Summary
Machine learning (ML/AI) is becoming one of the most commonly used approaches for predicting biological activities of molecules based on features derived from the molecular structures. Due to its efficiency, ML/AI models are especially useful to rank order and filter enumerated libraries with hundreds of thousands of molecules.
Classic ML models can be built with physio-chemical descriptors such as molecular weight (MW), number of aromatics rings, as well as topological descriptors, for example, atom path (APs) and extended connectivity fingerprints (ECFPs). Deep learning methods have emerged to represent molecules using fingerprints, smiles, and/or graphs. A systematic assessment is needed to compare different representations of molecules and types of ML models in terms of prediction accuracy and computational cost.
This internship position is located in South San Francisco, on site.
Key Responsibilities
Extract and curate the compound and appropriate experiment data from Genentech’s central small molecule database.
Build multiple QSAR models and evaluate on existing experimental data.
Implement command line and/or web application to conduct the model building and evaluation processes.
Program Highlights
Intensive 12-weeks, full time (40 hours per week) paid internship.
Program start dates are in May and June 2023.
Ownership of challenging and impactful business-critical projects.
Work with some of the most talented people in the biotechnology industry.
Final presentations of project work to senior leaders.
Lead or participate in intern committees to design and coordinate program events and initiatives.
Professional & personal development curriculum throughout the program, including networking opportunities, workshops, and panel discussions.
Participate in volunteer projects, social events, and team-building activities.
Who You Are
Education: Any Degree Level. Undergraduates who have completed at least one year of undergraduate coursework at a university, current graduate students (MS or PhD) who are available to work full time for the duration of the internship, or recent grads (within 2 years of receiving their last degree).
Preferred majors: Cheminformatics, Computational Chemistry, Computer Science with some Chemistry background.
Other Qualifications
Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
Proficiency with Python and Shell
Self-sufficient in scientific scripting and/or programming
Experience with machine learning/ AI model building and evaluation
Experience with data extraction and curation
Experience with one or more cheminformatics toolkits, e.g. RDKit, OpenEye
Along with the submission of your CV, a cover letter is optional.
Relocation benefits are not available for this job posting.
The expected salary range for this position based on the primary location for this position of California is $21.00 - $50.00 per hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.
Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.
Genentech requires all new hires to be fully vaccinated against COVID-19 as of their start date. This requirement is a condition of employment at Genentech, and it applies regardless of whether the position is located at a Genentech campus or is fully remote. If you are unable to receive the vaccine due to a disability or serious medical condition, or because it is prohibited as a result of your sincerely held religious beliefs, you will have an opportunity to request a reasonable accommodation.
