Molecular Property Prediction via Sparse Binary Matrix Representation and Convolutional Neural Networks

Abstract

A simple and interpretable matrix-based representation is presented for predicting molecular properties, specifically individual HOMO and LUMO frontier orbital energies and their resulting energy gaps, of functionalized organic molecules using a Convolutional Neural Network (CNN). Each molecule is encoded as a sparse binary matrix (SBMR) that captures the identity and position of substituents on a fixed molecular backbone. The model was initially benchmarked across four molecular families: n-butane, i-butane, cyclobutadiene, and quinone, achieving a combined RMSE of 4.0 kcal mol–1 for gap predictions compared to DFT-computed references, with over 85% of predictions falling within ±5% error. To contextualize this performance, the model was benchmarked against six established featurization methods spanning 2D topology and 3D physics-based approaches: the Coulomb Matrix (CM), Smooth Overlap of Atomic Positions (SOAP), 2D and 3D Message-Passing Neural Networks (MPNN), Random Forest with Morgan Fingerprints (RF-MF), and Uni-Mol+. The SBMR-CNN model demonstrates highly competitive accuracy, outperforming the CM, Uni-Mol+, and MPNN-2D benchmarks, while closely approaching the performance of the more computationally intensive MPNN-3D and SOAP descriptors, as well as the RF-MF model. This is achieved while offering distinct advantages through a dramatically smaller feature space and less stringent input data requirements. To demonstrate extensibility to complex catalytic systems, the architecture was applied to a combinatorial data set of 1,4-dihydropyridine derivatives, a class of redox mediators utilized in electrochemical and biochemical applications. For these highly functionalized heterocycles, the model successfully decoupled the energy gap into its constituent levels, predicting HOMO and LUMO energies with an RMSE of 2.8 and 2.5 kcal mol–1, respectively. The resulting framework couples high predictive accuracy with representational interpretability, offering a transparent and customizable tool for property prediction with direct applications in molecular screening, rational design, and electrocatalyst optimization.