Sr./Principal Computational Chemist

About Superluminal Medicines:

Superluminal Medicines (Superluminal) is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.

About the Role:

Working with us offers a unique opportunity to build and grow a fruitful career with the company and apply your computational chemistry expertise to impact human healthcare and the treatment of multiple diseases affecting patients worldwide. You will also be able to advance the state of drug discovery and development.

Responsibilities:

• Perform classical molecular modeling tasks such as MM optimization, equilibrium and accelerated MD simulations, in-silico docking, FEP calculations, etc.
• Perform giga-scale virtual screening
• Carry out cheminformatic analyzes, including queries of large databases based on similarity or substructures, MCS analysis, clustering, exemplars/outliers detection, etc.
• Curate datasets and create and train ML/AI models.
• Visualize concepts and results in 2D and 3D.
• Contribute to the development and implementation of novel computational methods for drug discovery.
• Deploy computational tools and create workflows in the cloud environments (GCP)
• Summarize your work and present actionable conclusions to interdisciplinary teams of computational chemists, ML/AI experts, medicinal chemists, and biologists at internal meetings.
• Stay up-to-date with the latest developments in computational chemistry to enhance our computational pipeline and expand our toolkits.

Qualifications:

• Ph.D. in computational chemistry, cheminformatics, or a related field with a minimum of 5 or 10 years of post-PhD experience in drug discovery for the senior or principal computational chemist role, respectively.
• Proven track record in applying molecular modeling methods: molecular mechanics and dynamics, the free energy of binding evaluation (TI/FEP)
• Strong experience with cheminformatics, including RDKit, OpenBabel, etc.
• Familiarity with ML/AI tools in computational chemistry, such as AlphaFold, DeepChem, PyTorch, etc.
• Experience using packages for molecular modeling, e.g., tools from Schrodinger or Cresset, Amber, NAMD, or other related software.
• Hands-on experience using visualization software, e.g., Pymol, or VMD 
• Experience coding, data analysis, and visualization tools in Python.
• Familiarity with public databases such as PubChem, PubMed, ChEMBL, UniProt, etc., and practical experience in using APIs to query these databases programmatically
• Familiarity with cloud computing environments, such as AWS, GPC, etc.
• Excellent communication and interpersonal skills. Ability to work collaboratively and communicate effectively with scientists from diverse areas of expertise.