An epidemic is sweeping through cheminformatics (and machine learning) research: ugly results tables. These tables are typically bloated with metrics (such as regression and classification metrics next to each other), vastly differing tasks, erratic bold text, and many models. As a consequence, results become difficult to analyse and interpret. Additionally, it is rare to see convincing evidence, such as statistical tests, for whether one model is ‘better’ than another (something Pat Walters has previously discussed). Tables are a practical way to present results and are appropriate in many cases; however, this practicality should not come at the cost of clarity.
The terror of ugly tables extends to benchmark leaderboards, such as Therapeutic Data Commons (TDC). These leaderboard tables do not show:
- whether differences in metrics between methods are statistically significant,
- whether methods use ensembles or single models,
- whether methods use classical (such as Morgan fingerprints) or learned (such as Graph Neural Networks) representations,
- whether methods are pre-trained or not,
- whether pre-trained models are supervised, self-supervised, or both,
- the data and tasks that pre-trained models are pre-trained on.
This lack of context makes meaningful comparisons between approaches challenging, obscuring whether performance discrepancies are due to variance, ensembling, overfitting, exposure to more data, or novelties in model architecture and molecular featurisation. Confirming the statistical significance of performance differences (under consistent experimental conditions!) is crucial in constructing a more lucid picture of machine learning in drug discovery. Using figures to share results in a clear, non-tabular format would also help.
Statistical validation is particularly relevant in domains with small datasets, such as drug discovery, as the small number of test samples leads to high variance in performance between different splits. Recent work by Ash et al. (2024) sought to alleviate the lack of statistical validation in cheminformatics by sharing a helpful set of guidelines for researchers. Here, we explore implementing some of the methods they suggest (plus some others) in Python.
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