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The application of machine learning (ML) techniques in computationalchemistry has led to significant advances in predicting molecular properties,accelerating drug discovery, and material design. ML models can extract hiddenpatterns and relationships from complex and large datasets, allowing for theprediction of various chemical properties with high accuracy. The use of suchmethods has enabled the discovery of molecules and materials that werepreviously difficult to identify. This paper introduces a new ML model based ondeep learning techniques, such as a multilayer encoder and decoderarchitecture, for classification tasks. We demonstrate the opportunitiesoffered by our approach by applying it to various types of input data,including organic and inorganic compounds. In particular, we developed andtested the model using the Matbench and Moleculenet benchmarks, which includecrystal properties and drug design-related benchmarks. We also conduct acomprehensive analysis of vector representations of chemical compounds,shedding light on the underlying patterns in molecular data. The models used inthis work exhibit a high degree of predictive power, underscoring the progressthat can be made with refined machine learning when applied to molecular andmaterial datasets. For instance, on the Tox21 dataset, we achieved an averageaccuracy of 96%, surpassing the previous best result by 10%. Our code ispublicly available at https://github.com/dmamur/elembert.

Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties