MoleHD: Efficient Drug Discovery using Brain Inspired Hyperdimensional Computing

Abstract

In this paper, we propose MoleHD, an efficient learning model based on brain-inspired hyperdimensional computing (HDC) for molecular property prediction. We develop HDC encoders to project SMILES representation of a molecule into high-dimensional vectors that are used for HDC training and inference. We perform an extensive evaluation using 29 classification tasks from 3 widely-used molecule datasets (Clintox, BBBP, SIDER) under three splits methods (random, scaffold, and stratified). By a comprehensive comparison with 8 existing learning models, we show that MoleHD achieves highest ROC-AUC score on random and scaffold splits on average across 3 datasets and achieve second-highest on stratified split. More importantly, MoleHD achieves such performance with significantly reduced computing cost: no back-propagation needed, only around 10 minutes training time using CPU.MoleHD is open-sourced and available at https://github.com/VU-DETAIL/MoleHD.