
Small Molecule Discovery Expert
1 week ago
The Role: Pattern is building a next-generation AI-driven drug discovery platform that combines cutting-edge structural modeling, generative design, and reinforcement learning agents to explore the vast chemical space for novel small-molecule therapeutics.
This is a strategic non-lab role focusing on bridging AI outputs with biological and chemical insight.
- Lead ligand pose prediction workflows using state-of-the-art AI and computational docking tools (e.g. DiffDock, EquiBind, Glide, GOLD).
- Evaluate protein-ligand binding interactions for fit, contact quality, and structural plausibility.
- Collaborate with AI/deep learning engineers to refine de novo molecular generation strategies using models such as REINVENT, Pocket2Mol, and diffusion-based 3D generators.
- Apply drug-likeness, ADMET, novelty, and selectivity criteria to prioritize compound candidates.
- Integrate binding mode insights with biological context from Pattern's Agentix Knowledge Graph to align compounds with target mechanism-of-action.
- Generate clear, actionable compound selection lists for partner synthesis and in-vitro testing.
Required Skills and Qualifications:
- PhD or Masters in Medicinal Chemistry, Chemical Biology, Computational Chemistry, or related discipline.
- Proven experience in structure-based drug design and ligand pose evaluation.
- Strong working knowledge of protein-ligand binding principles (H-bonds, hydrophobic contacts, electrostatics, shape complementarity).
- Familiarity with AI/ML-based molecular design platforms.
Benefits:
You'll be joining at the frontier of AI-guided drug discovery, working side-by-side with deep learning experts to build a platform capable of efficiently searching through 10^60 chemical possibilities.
Job Requirements:
- Excellent communication skills and ability to work cross-functionally with AI engineers, biologists, and medicinal chemists.
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