LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila Sciences is seeking a Scientist, Computational Chemistry, Drug Discovery to help guide, evaluate, and improve AI-driven drug discovery workflows. This person will bring practical computational chemistry experience in a drug discovery context and will work alongside AI systems to make sure agent-generated optimization plans, compound prioritizations, and modeling workflows are chemically and scientifically sensible.
What they're looking for
- PhD or equivalent experience in computational chemistry, chemistry, cheminformatics, molecular modeling, biophysics, or a related field
- Strong practical experience applying computational chemistry in a drug discovery context, including active program support or leadership
- Demonstrated history of modeling protein-ligand binding and using those models to inform discovery decisions
- Working knowledge across docking, virtual screening, SAR modeling, conformer generation, pharmacophore modeling, QSAR, ADMET or property prediction, and cheminformatics
- Strong medicinal chemistry experience and the ability to reason about compound optimization, SAR, developability, and synthetic or experimental tradeoffs
- Fluency in Python and hands-on experience building open-source computational chemistry workflows with libraries such as RDKit, Biopython, OpenMM, MDAnalysis, or comparable tools
More about this role
Lila Sciences is seeking a Scientist, Computational Chemistry, Drug Discovery to help guide, evaluate, and improve AI-driven drug discovery workflows. This person will bring practical computational chemistry experience in a drug discovery context and will work alongside AI systems to make sure agent-generated optimization plans, compound prioritizations, and modeling workflows are chemically and scientifically sensible. The primary mandate is to make agent-guided discovery scientifically useful, while also contributing directly to live drug discovery programs when human computational chemistry leadership is needed.
This is a role for someone who can look at an agent-driven drug optimization rollout and answer hard questions: Does this plan make sense? Are the right compounds being prioritized? Are the right modeling tools being used? What additional tools, constraints, or review steps should be built so agents can make better discovery decisions? When needed, this person can also step into an active discovery effort and lead the computational chemistry strategy for pursuing a drug program. The role spans docking, virtual screening, SAR modeling, molecular property prediction,...
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