Anticancer Drug Response Eval

anticancer-drug-response-eval

Predicting Drug-Drug Interactions using Deep Generative Models on Graphs — Ngo et al. (2022) (arXiv:2209.09941, 2022)

What this evaluates

Evaluates a model's ability to predict anticancer drug responses (IC50 scores) between drugs and cell lines. It probes the model's capacity to perform weighted link prediction/regression on a multimodal graph combining drug and cell line similarities.

Datasets

• CCLE Dataset — total ?; splits: train (-1), val (-1), test (-1); repo https://github.com/HySonLab/drug-interactions

Metrics

• MSE (primary) — range: [0, inf)
  • Mean Squared Error between predicted and ground-truth edge weights: sum (s_hat_e(v_i, v_j) - s_e(v_i, v_j))^2.

Input / output format

Input: Undirected multimodal graph of drug and cell line nodes. Edge weights represent pairwise similarity scores (9 combinations of 3 similarity types per modality).

Output: Continuous real-valued score (predicted edge weight) representing the logarithm of the IC50 score for a drug-cell line pair.

Scoring recipe

for edge in test_edges:
    pred_score = model.predict(edge.u, edge.v)
    true_score = edge.weight
    mse = (pred_score - true_score) ** 2
return mean(mse)

Common pitfalls

• Task is framed as link prediction but targets continuous regression (log IC50), not binary classification.
• Graph construction uses similarity scores as edge weights, meaning missing links must be reconstructed as continuous values.

Evidence (verbatim from paper)

where s_hat_e(v_i, v_j) indicates the predicted edge weights between node v_i and v_j, whereas s_e(v_i, v_j) are their ground truths. ... L = sum (s_hat_e(v_i, v_j) - s_e(v_i, v_j))^2 - sum lambda_v D_KL

Citation

@misc{ngo2022predicting,
  title={Predicting Drug-Drug Interactions using Deep Generative Models on Graphs},
  author={Ngo et al. (2022)},
  year={2022},
  note={arXiv:2209.09941}
}

• arXiv: 2209.09941