Assessing the performance of implicit ensembling of neural networks for proteochemometric modeling with RandomNets
Year: 2026
Type: preprint
Abstract: RandomNets, a recently introduced neural-network architecture that emulates the feature and sample bagging of Random Forests through an implicit, vectorised ensemble, has shown promising performance for quantitative structure–activity relationship (QSAR) modelling, but so far only on random data splits. Here we evaluate RandomNets in a proteochemometric (PCM) setting on two benchmarks: an original dataset (BtH) and a newly prepared dataset derived from ChEMBL 35, comprising 571,197 interactions across 382,493 unique compounds and 2,023 unique targets. Models were built from Morgan fingerprints together with additional physicochemical and protein descriptors, and assessed under both random and temporal splits. All models were trained within QSPRpred, enabling reproducible and benchmark-agnostic comparison against the best-performing PCM models from BtH, a deep neural network (DNN_PCM) and a Random Forest (RF_PCM). RandomNets with two layers gave the best overall performance, notably improving on the Matthews correlation coefficient (MCC) of the DNN baseline for the temporal split by 5.55% on the original dataset and 8.73% on the new dataset, while taking 2× less time to train on the original dataset and the same time on the new dataset. These results affirm the potential of RandomNets and extend it to PCM modelling.
Source: ChemRxiv
Institution: Leiden University
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212221562