Ligand Design & Pose Prediction Lead

About the Role

Pattern is building a next-generation AI-driven drug discovery platform that integrates state-of-the-art structural modeling, generative design, and reinforcement learning agents to explore the vast chemical space for novel small-molecule therapeutics.

We are seeking a Ligand Design & Pose Prediction Lead to guide de novo small-molecule exploration, interpret protein–ligand binding predictions, and prioritize compounds for synthesis / testing. You will work in close partnership with a deep learning specialist to combine cutting-edge AI tools with your medicinal chemistry and structure-based design expertise.

This is a strategic, non-lab role — your primary focus will be to bridge AI outputs with biological and chemical insight, ensuring the most promising designs move forward.

Key Responsibilities

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.

Contribute to feedback loops by incorporating experimental assay data into ongoing model optimization.

Present binding hypotheses, SAR rationale, and prioritization strategies to cross-functional teams.

Qualifications Required

PhD or Masters in Medicinal Chemistry, Chemical Biology, Computational Chemistry, or related discipline (or MSc + 3+ years of relevant experience).

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.

Ability to work with predicted protein structures (AlphaFold / OpenFold) and assess binding pockets.

Experience applying drug-likeness rules and property-based filtering in lead prioritization.

Excellent communication skills and ability to work cross-functionally with AI engineers, biologists, and medicinal chemists.

Preferred

Familiarity with pharmacophore modeling and pocket geometry analysis.

Experience in multi-objective optimization (binding, ADMET, novelty).

Exposure to reinforcement learning-driven compound optimization workflows.

Why Join Us?

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. Your expertise will directly shape the quality and novelty of our candidate compounds, accelerating the path from target to therapy.