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.