Design a reaction-factor prediction system

Quick Overview

This question evaluates competency in designing end-to-end machine learning systems for molecular pair regression tasks, including data quality and normalization, molecular representation and feature engineering, model selection, evaluation metrics, uncertainty estimation, and production considerations.

Design a reaction-factor prediction system

Company: Google

Role: Machine Learning Engineer

Category: ML System Design

Difficulty: hard

Interview Round: Technical Screen

You are given a dataset of chemical reactions with columns: molecule1_name, molecule2_name, reaction_factor (real-valued). Design an end-to-end approach to predict reaction_factor. Cover: 1) EDA: checks/plots for label distribution, missingness, outliers, duplicates, leakage; normalization of molecule naming (synonyms, casing), and detection of duplicate reactions in reversed order. 2) Feature engineering: converting molecule names to usable representations (lookups to SMILES/InChI, RDKit descriptors, learned embeddings, graph encodings); constructing pairwise interaction features (concatenation, differences, products, attention/cross features); handling unseen molecules and symmetry (A+B ≈ B+A). 3) Data splitting: propose and justify random, grouped-by-molecule, leave-one-molecule-out (cold-start), scaffold-based, or time-based splits; explain how each estimates generalization and prevents leakage. 4) Modeling: baselines (linear/elastic net), tree ensembles (XGBoost/LightGBM), neural models (MLP on descriptors, Transformer on SMILES, GNN/Graph Transformer on molecular graphs); uncertainty estimation and calibration. 5) Training and evaluation: loss/metrics (MAE, RMSE, R²), cross-validation strategy, hyperparameter search, early stopping, error analysis by scaffold/family/frequency. 6) Generalization and robustness: standardization, regularization, data augmentation (e.g., SMILES enumeration), pretraining on large molecular corpora, semi-supervised learning, ensembling, adversarial validation, domain shift checks; design of ablations and offline A/B comparisons. 7) Production: feature pipelines, reproducibility, monitoring, retraining triggers, and scientific safety considerations.

Overview: This question evaluates competency in designing end-to-end machine learning systems for molecular pair regression tasks, including data quality and normalization, molecular representation and feature engineering, model selection, evaluation metrics, uncertainty estimation, and production considerations.

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Sep 6, 2025
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End-to-End System Design: Predicting a Reaction Factor from Molecule Pairs

Context and goal

  • You have a tabular dataset with columns:
    • molecule1_name (string)
    • molecule2_name (string)
    • reaction_factor (real-valued target)
  • Task: Design an end-to-end ML approach to predict reaction_factor for a pair of molecules. Assume you can query public chem informatics resources (e.g., to map names to SMILES/InChI) and use standard cheminformatics tooling.

Explicit assumptions (state any others you need)

  • reaction_factor is a continuous scalar (e.g., yield, rate, affinity) with potential skew and outliers.
  • The order of molecules is not expected to change the physics significantly (A+B ≈ B+A), but the dataset may contain both orders.
  • No reaction conditions are provided; if present, treat them as additional features but avoid leakage.

Requirements

  1. Exploratory Data Analysis (EDA)
    • Label checks: distribution/transformations; outliers; replicate consistency.
    • Data quality: missingness; duplicates; reversed-order duplicates; potential leakage.
    • Name normalization: casing, synonyms, canonical identifiers; detect inconsistent naming and collisions.
  2. Feature engineering
    • Map names to molecular representations (SMILES/InChI), compute descriptors (RDKit), fingerprints, embeddings, or graph encodings.
    • Construct pairwise features: concatenation, symmetric aggregations (sum/max/abs-diff/product), cross/attention features.
    • Handle unseen molecules; enforce symmetry (A+B ≈ B+A) in the design.
  3. Data splitting
    • Propose and justify: random split; grouped-by-molecule split; leave-one-molecule-out (cold-start); scaffold-based split; time-based split.
    • Explain what each split estimates and how it mitigates leakage.
  4. Modeling
    • Baselines: linear/ridge/elastic net.
    • Tree ensembles: XGBoost/LightGBM/CatBoost.
    • Neural models: MLP on descriptors; Transformer on SMILES; GNN/Graph Transformer on molecular graphs with a pairwise architecture.
    • Uncertainty estimation and calibration strategies.
  5. Training and evaluation
    • Loss and metrics: MAE, RMSE, R²; robust losses.
    • Cross-validation strategy aligned to your split choices; hyperparameter search; early stopping.
    • Error analysis: by scaffold/family/frequency and other covariates.
  6. Generalization and robustness
    • Standardization, regularization.
    • Data augmentation (e.g., SMILES enumeration), pretraining on large corpora, semi-supervised learning, ensembling.
    • Adversarial validation, domain-shift checks.
    • Ablations and offline A/B comparisons.
  7. Production considerations
    • Feature pipelines, caching, reproducibility/versioning.
    • Monitoring (data/label drift, uncertainty), retraining triggers.
    • Scientific safety checks and guardrails.

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