Senior Computational Chemist
1 month ago
Panda Intelligence is a cutting-edge biotech company with roots in MIT and a prominent tech accelerator in the US. We are seeking a highly skilled Senior Computational Chemist to join our team and contribute to our AI-driven small molecule drug discovery pipeline.
Key Responsibilities:
- Design and optimize novel therapeutic candidates using GANs and other deep learning architectures.
- Collaborate with interdisciplinary teams to integrate 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.
Requirements:
- 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.
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