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

• URL: http://arxiv.org/abs/2309.09355v1
• Date: Sun, 17 Sep 2023 19:41:32 GMT
• Title: Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties
• Authors: Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst
• Abstract summary: This paper introduces a new machine learning model based on deep learning techniques, such as a multilayer encoder and decoder architecture, for classification tasks. We demonstrate the opportunities offered by our approach by applying it to various types of input data, including organic and inorganic compounds. The models used in this work exhibit a high degree of predictive power, underscoring the progress that can be made with refined machine learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of machine learning (ML) techniques in computational chemistry has led to significant advances in predicting molecular properties, accelerating drug discovery, and material design. ML models can extract hidden patterns and relationships from complex and large datasets, allowing for the prediction of various chemical properties with high accuracy. The use of such methods has enabled the discovery of molecules and materials that were previously difficult to identify. This paper introduces a new ML model based on deep learning techniques, such as a multilayer encoder and decoder architecture, for classification tasks. We demonstrate the opportunities offered by our approach by applying it to various types of input data, including organic and inorganic compounds. In particular, we developed and tested the model using the Matbench and Moleculenet benchmarks, which include crystal properties and drug design-related benchmarks. We also conduct a comprehensive analysis of vector representations of chemical compounds, shedding light on the underlying patterns in molecular data. The models used in this work exhibit a high degree of predictive power, underscoring the progress that can be made with refined machine learning when applied to molecular and material datasets. For instance, on the Tox21 dataset, we achieved an average accuracy of 96%, surpassing the previous best result by 10%. Our code is publicly available at https://github.com/dmamur/elembert.

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