Ligand design

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., Diff Dock, Equi Bind, 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, Pocket2 Mol, and diffusion-based 3 D 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 Ph D 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 (Alpha Fold/Open Fold) 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.