Umesh Arampath

Using UV absorption wavelength (λmax) prediction as a testbed across 18,415 solute-solvent pairs (after the Greenman/Song duplicate-handling protocol) with stratified 5-fold cross-validation and two external data sets totaling over 40,000 molecules, this work compares five models spanning four architecture families (Random Forest, XGBoost, a directed message-passing GNN, a BiGRU, and a pretrained Transformer) and finds that Random Forest is statistically indistinguishable from the best deep learning model for screening within known chemical space (RMSE 31.50 ± 1.47 vs 33.15 ± 3.27 nm, p = 0.16) while training in 15 min on a CPU, whereas on 16 novel UV-absorbing compounds the GNN (MAE 26.2 nm), Transformer (26.3 nm), and BiGRU (28.6 nm) all outperform RF (38.

Using UV absorption wavelength (λmax) prediction as a testbed across 18,415 solute-solvent pairs (after the Greenman/Song duplicate-handling protocol) with stratified 5-fold cross-validation and two external data sets totaling over 40,000 molecules, this work compares five models spanning four architecture families (Random Forest, XGBoost, a directed message-passing GNN, a BiGRU, and a pretrained Transformer) and finds that Random Forest is statistically indistinguishable from the best deep learning model for screening within known chemical space (RMSE 31.50 ± 1.47 vs 33.15 ± 3.27 nm, p = 0.16) while training in 15 min on a CPU, whereas on 16 novel UV-absorbing compounds the GNN (MAE 26.2 nm), Transformer (26.3 nm), and BiGRU (28.6 nm) all outperform RF (38.