Machine Learning Scientist I/II, Medicinal Chemistry & Lead Optimization

Lila Sciences • Cambridge, MA

22d • $176,000 - $304,000

About The Position

Join our Drug Discovery group to build and deploy ligand-based AI that turns noisy, real-world assay data into decisive design guidance for hit-to-lead and lead optimization. You’ll create QSAR models, retrosynthesis-aware generative design tools, and active-learning loops that partner with medicinal chemists to deliver better compounds, faster. This role complements our structure-based docking team by focusing on assay-driven, synthesis-constrained optimization—even when structures are uncertain or unavailable—ultimately accelerating DMTA cycles and improving candidate quality.

Requirements

• Strong proficiency in Python and modern ML (PyTorch/JAX/TF, scikit-learn, XGBoost/CatBoost), with experience training at scale and deploying end-to-end pipelines.
• Deep experience in ligand-based modeling (QSAR/QSPR, multi-task learning, uncertainty and applicability domain, calibration) and ADMET prediction for medicinal chemistry.
• Solid grasp of medicinal chemistry principles: SAR development, bioisosteres, property tuning (pKa/logD/PSA), selectivity design, and liability mitigation (CYP, hERG, reactivity, permeability, solubility).
• Cheminformatics and data tooling: RDKit, Chemprop/DeepChem, conformer generation, fingerprints/descriptors, ELN/LIMS integration, and assay data processing/curve-fitting.
• Retrosynthesis and synthesis planning: Familiarity with template-based/template-free methods, route scoring, reaction/yield/condition prediction, building block catalogs, and makeability constraints.
• Active learning and design-of-experiments: Bayesian optimization, diversity sampling, and portfolio-aware selection under experimental and synthesis budgets.
• Ability to design rigorous, leakage-controlled benchmarks and prospective validations; experience with scaffold/time splits and activity-cliff-aware evaluation.
• Strong self-starter with excellent attention to detail and clear communication; able to collaborate tightly with chemists and biologists.
• Demonstrated industry experience or academic achievement.

Nice To Haves

• PhD in Chemoinformatics, Medicinal Chemistry, Computational Chemistry, Computer Science, or related field with a strong publication record in ML/drug discovery venues.
• Experience building synthesis-aware generative models and integrating retrosynthesis into design loops; familiarity with tools like ASKCOS/AiZynth-style planners or equivalent.
• Track record improving DMTA cycle time and MPO outcomes in live programs; integration with procurement and automated synthesis platforms.
• Expertise with MMPA, activity-cliff handling, conformal prediction, and applicability-domain diagnostics in production.
• Experience triaging HTS/DEL data, PAINS/aggregator/covalent liability filters, and off-target/polypharmacology prediction.
• MLOps for cheminformatics: data versioning, experiment tracking, model serving/monitoring, and cloud/HPC scaling.

Responsibilities

• Ligand-based QSAR modeling: Develop multi-task and transfer-learned models for potency, selectivity, and developability (e.g., solubility, permeability, clearance, CYP/hERG, safety liabilities) using graph/message-passing, and conformer-aware features. Handle activity cliffs, applicability domain, and calibration for robust decision-making.
• Assay-driven hit triage and prioritization: Build models that learn from HTS, DEL, and follow-up assays; robust curve-fitting (4PL/5PL), plate/batch effect correction, dose–response QC, and time-split/scaffold-split evaluations to ensure prospective reliability.
• Closed-loop DMTA and MPO: Create active learning and Bayesian optimization strategies to propose the next best analogs under multi-parameter objectives (potency, selectivity, exposure, safety, IP). Incorporate uncertainty, diversity, and experimental cost to maximize information gain per cycle.
• Synthesis-aware design and retrosynthesis: Integrate template-based and template-free retrosynthesis with reaction prediction, condition and yield modeling, building-block availability, and cost/time/risk scoring. Make design suggestions that are directly makeable and prioritize routes compatible with internal/partner capabilities.
• Generative and enumerative libraries: Build BRICS/RECAP/fragment-linking enumerations and property-conditioned generative models (diffusion/RL/flow) that respect synthetic constraints and matched molecular pair (MMP) rules for local SAR exploration and scaffold hopping.
• SAR mining and explainability: Automate MMP analysis, local SAR maps, and substructure attributions to surface chemist-actionable insights; link assay deltas to specific modifications and highlight potential bioisosteres and de-risking moves.
• Data foundations: Establish cheminformatics pipelines for standardization (tautomer/salt/charge), deduplication, structure normalization, and assay/ELN/LIMS ingestion; define ontologies and metadata for traceability and reproducibility.
• Rigorous evaluation and deployment: Design leakage-safe splits (scaffold, temporal, series-aware), conformal prediction for calibrated decisions, and prospective tests. Ship APIs and tools that integrate with medchem workflows, procurement, and automated synthesis.
• Cross-functional partnership: Work closely with medicinal chemists, DMPK, biology, and automation to translate TPPs into modeling objectives and to operationalize model recommendations in real make–test cycles. Collaborate with structure-based colleagues to fuse physics- and assay-derived signals where beneficial.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 101-250 employees