ArticleJournal of computer-aided molecular design2025
LiteBoost: a lightweight and explainable boosting model for predicting polymer density from SMILES data.
Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurately predicting polymer density from SMILES strings remains challenging due to the small size, high noise, and chemically diversity of typical datasets. We introduce LiteBoost, a deliberately minimalist gradient boosting model that employs shallow, three-level symmetric trees and exposes only two tunable hyperparameters (n_estimators and learning_rate). Using a curated dataset of 613 polymers, we benchmark LiteBoost against ExtraTrees, XGBoost, LightGBM, and CatBoost, optimizing each with 100-1000 Optuna trials and evaluating performance across seven complementary metrics: R
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