Senior Computational Chemist – Cambridge, MA
Panda Intelligence is currently representing a novel oncology-focused Biotech with roots in MIT and one of the most prominent tech accelerators in the US, with the team currently seeking a Senior Computational Chemist with machine learning expertise ahead of advancing their AI-driven small molecule drug discovery pipeline. You will be collaborating closely with medicinal chemists, biologists, and data scientists to design and optimize novel therapeutic candidates but will be coming in as the first computational hire under the Head of Data Science, with potential to play a pivotal role in defining the overall data strategy of the company and progress rapidly to management.
Key Responsibilities:
- Use GANs and other deep learning architectures to generate novel small molecule drug candidates with optimized pharmacological properties (e.g., bioavailability, binding affinity, ADMET).
- Work with interdisciplinary teams to design and improve the properties of lead compounds, integrating computational models with experimental data from medicinal chemists and biologists.
- Develop and refine predictive models for molecular properties such as solubility, permeability, and toxicity using AI and quantum chemistry techniques.
- Leverage computational tools to perform virtual screening against biological targets, identifying potential drug candidates from large chemical libraries.
- Analyze chemical and biological datasets to generate insights that inform drug discovery strategies, helping guide experimental workflows.
Required Qualifications and Experience:
- Ph.D. in Computational Chemistry, Cheminformatics, or a related field.
- Prior experience in a biotech startup or pharmaceutical R&D setting.
- 5+ years of experience in the application of computational chemistry to drug discovery, with a strong emphasis on small molecules.
- Hands-on experience with GANs or other deep learning models in molecular design or optimization.
- Strong programming skills in Python, with experience in libraries like TensorFlow, PyTorch, or other machine learning frameworks.
- Proficiency with molecular modeling software, such as Schrodinger, OpenEye, or MOE, and quantum chemistry methods (e.g., DFT, QM/MM).
- Expertise in molecular dynamics simulations, virtual screening, and ligand-protein docking techniques.
- Familiarity with ADMET property prediction and structure-based drug design principles.
- Ability to work collaboratively in a cross-functional team environment with excellent communication.
Preferred Qualifications:
- Experience working in the oncology or rare diseases therapeutic areas.
- Knowledge of other generative models (e.g., VAEs, transformers) and their application in drug discovery.
- Experience with cloud computing platforms (e.g., AWS, GCP) for scaling computational workloads.