At Atomic AI, the business problem is to pioneer new frontiers in drug discovery by targeting RNA and treating previously undruggable diseases
Requirements
- Strong Python programming skills, including proficiency with cheminformatics-focused libraries such as NumPy, pandas, and RDKit
- Strong foundation in statistics and experience with conducting statistical analysis of large-scale datasets
- Experience developing, validating, and applying ligand-based modeling approaches, including shape-based methods (e.g., ROCS) and QSAR modeling
- Conceptual understanding of ML model development and evaluation
- Experience with high-throughput experimental assays used for evaluating RNA-SM interactions
- Expertise with structure-based methods for computational drug design and optimization
- Experience developing custom machine learning models (e.g., using scikit-learn) and with deep learning frameworks (such as JAX or PyTorch)
Responsibilities
- Serve as an internal expert on RNA-small molecule (RNA-SM) interactions and small molecule design within the ML team
- Perform statistical analyses to interpret biological variability and extract actionable insights from RNA-SM interaction datasets
- Design and implement computational pipelines for analyzing compound properties in RNA-SM datasets, performing virtual screening, and developing predictive models (e.g., QSAR)
- Utilize data analysis and ligand-based modeling to support early-stage drug discovery, including small molecule library design and rational drug design strategies
- Curate small-molecule datasets for training of ML models, help evaluate model performance, and provide directions for improvement
- Collaborate closely with the internal wetlab team, influencing the design of experimental assays to probe RNA-SM interactions
- Drive the development of in silico screening pipelines
Other
- Ph.D. in Computational Chemistry, Chemical Informatics, Chemistry, Biophysics, or a closely related field (or equivalent practical experience)
- Excellent communication and presentation skills, with the ability to clearly and effectively share technical information with colleagues
- Three days in-person at our South San Francisco office
- Ability to work in a hybrid position
- Commitment to equal employment opportunity regardless of race, color, ancestry, national origin, religion, sex, age, sexual orientation, gender identity and expression, marital status, disability, or veteran status
