axiombio - Computational Scientist (Medicinal Chemistry)
Requirements
• We are looking for someone who can combine medicinal chemistry judgment with computational depth. • You have an advanced degree in chemistry, computational chemistry, cheminformatics, medicinal chemistry, chemical biology, or equivalent experience inside a drug discovery organization. • You might identify as a computational chemist, cheminformatics scientist, ML for chemistry researcher, medicinal chemist with strong computational skills, or drug discovery scientist who became deeply technical. • You understand how real drug programs move from hit discovery to lead optimization to candidate selection. • You can reason about potency, selectivity, physicochemical properties, ADME, PK, exposure, safety margins, and clinical translatability. • You are excited by the challenge of connecting chemical structure to human outcomes. • You understand the limitations of current preclinical safety models and have strong opinions about how they should be improved. • You are comfortable analyzing large chemical datasets and drawing conclusions from a combination of data science, chemistry, and biological reasoning. • You can work directly with pharma customers, earn the trust of senior drug hunters, and communicate technical insights clearly. • You want to build tools that are not just scientifically interesting, but actually used to make decisions in real drug discovery programs. • We do not expect every candidate to have all of these, but we are especially excited by experience with: • Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks • RDKit, Datamol, DeepChem, or related cheminformatics tooling • Chemical structure processing, standardization, salt stripping, stereochemistry handling, scaffold analysis, similarity search, clustering, and molecular fingerprints • Large-scale chemical dataset curation and quality control • QSAR, molecular property prediction, ADME modeling, exposure modeling, or toxicity prediction • Dose-response modeling, curve fitting, calibration, benchmarking, uncertainty analysis, and model error analysis • SQL, cloud data workflows, and large-scale data processing • Drug discovery datasets involving targets, assays, potency, selectivity, ADME, PK, toxicology, or clinical outcomes • Scientific presentation and storytelling for medicinal chemists, toxicologists, and drug discovery leadership
Responsibilities
• You will sit at the center of Axiom’s chemistry, biology, modeling, and customer work. • Lead the analysis of model outputs across chemical series, targets, modalities, mechanisms, and clinical toxicity endpoints. • Identify where Axiom’s models perform well, where they fail, and what those failures reveal about chemistry, biology, exposure, or missing data. • Work with ML researchers to improve models that predict human toxicity as a function of chemical structure, in vitro potency, biological response, dose, Cmax, ADME, and clinical context. • Analyze large-scale chemistry datasets across thousands to hundreds of thousands of compounds for model training, evaluation, benchmarking, and dataset design. • Clean, curate, and structure chemical data, including compound identifiers, structures, salts, stereochemistry, dose/exposure information, ADME properties, targets, annotations, and clinical outcomes. • Use medicinal chemistry intuition to interpret model predictions, understand structure–toxicity relationships, and identify chemically meaningful patterns. • Partner directly with top drug hunters at leading pharma and biotech companies to interpret model outputs and help them make better program decisions. • Help design new experimental and molecular datasets based on model failures, customer needs, chemical space gaps, and real-world drug discovery use cases. • Work with Axiom’s mechanistic agent to connect chemical structure, biological readouts, phenotypic similarity, clinical outcomes, and proposed mechanisms of toxicity. • Influence active drug programs by helping teams understand whether toxicity risk is driven by exposure, potency, off-target biology, reactive metabolites, transporters, mitochondrial liability, cholestasis, immune mechanisms, or other drivers. • Shape Axiom’s product by translating customer feedback into better model outputs, visualizations, analyses, and workflows for medicinal chemists and toxicologists. • Help define how the best drug hunters in the world will use AI to design safer medicines.
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