Research Scientist II/Senior, Computational Chemistry

Iambic Therapeutics, Inc•San Diego, CA

11h•$124,000 - $172,000•Hybrid

About The Position

We are seeking a skilled and highly motivated Computational Chemist to join our Computational Sciences group at Iambic Therapeutics. You will be a key player in the application of advanced ligand and structure-based techniques to drive our drug discovery efforts. You will play an active role in the research teams pursuing important therapeutics across multiple indications. You will work in a cross-functional team, collaborating closely with machine learning scientists, medicinal chemists and biologists to apply cutting edge SBDD methods across hit identification, lead optimization, and candidate selection phases. This is a hands-on role, requiring technical expertise in computational chemistry, experience with or strong interest in applying modern co-folding approaches, familiarity with the drug discovery process and a proactive drug-hunter mindset. This role will involve engagement in external partnerships. This position will be based in our San Diego Headquarters, with exceptions for outstanding candidates to work remotely.

Requirements

PhD or equivalent degree in computational chemistry (or related field) with 2+ years of relevant experience in drug discovery
Experience supporting active small molecule drug discovery programs
Experience with ligand and structure based design tools
Proficiency in Python for scientific computing, data analysis, and computational workflow development
Excellent written and verbal communication skills; must be able to articulate complex scientific concepts, technical challenges, and results clearly and concisely to diverse audiences.

Nice To Haves

Industry experience supporting active drug discovery programs
Experience with molecular modeling platforms such as MOE, Schrödinger, OpenEye, or equivalent tools
Familiarity with MD simulation, molecular docking and advanced binding affinity prediction methods (e.g. MM/GBSA, FEP)
Experience with ML-augmented small molecule discovery

Responsibilities

Apply a range of ligand and structure-based approaches across hit identification, hit to lead and lead optimization phases (e.g. cheminformatics, pharmacophore modeling, docking and shape-based based virtual screening, de novo molecular design, MD simulation)
Guide design cycles through the identification of structure-activity relationships and the integration of computational predictions with bioactivity data
Partner with medicinal chemists to design, evaluate and prioritize virtual libraries, and propose actionable molecular designs
Partner with ML scientists to integrate SBDD methods with our proprietary transformer-based property prediction technology (Enchant)
Leverage and evolve our proprietary protein-ligand co-folding technology (NeuralPLexer) to yield novel and actionable structural hypotheses
Translate scientific questions into computational workflows and deliver actionable results
Present results to internal project teams and external partners, explaining methods, implications and recommendations
Develop, validate, and maintain computational workflows and tools