Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang, Hong; Hou, Bo; Jiang, Haobo; Zhang, Jingchao

doi:10.1002/wcms.1450

Cited by 77 publications

(57 citation statements)

References 197 publications

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“…In principle, it can be considered that the remaining compounds belonged to the structure type labeled as "others", since these compounds did not match the provided definition of the crystal structure types. For this second test, 4S4O, 6S4O, 6S8O, and 6S10O ANNs were tested with 12,264,19,229,18,179, and 16,667 compounds, respectively. The difference in the number of compounds used in the second test depended on the number of compounds in the traval and test sets.…”

Section: Classification Of Crystal Compoundsmentioning

confidence: 99%

“…In recent years, machine learning algorithms have irrupted as an alternative tool to model the properties and structure of materials [1][2][3][4][5][6][7][8][9][10][11]. These algorithms have allowed scientists to work with large particle systems at shorter times and lower computational costs with respect to the recurred quantum methods [12][13][14][15]. Agrawal and Choudhary [16] have suggested that machine learning constitutes nowadays a fourth modeling paradigm in science, which relies on the information stocked in large databases.…”

Section: Introductionmentioning

confidence: 99%

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Crystal-Site-Based Artificial Neural Networks for Material Classification

2021

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In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.

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Section: Classification Of Crystal Compoundsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crystal-Site-Based Artificial Neural Networks for Material Classification

2021

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“…The advent of Machine Learning (ML) and Deep Learning (DL) approaches have created a path towards solving previously challenging unsolved problems in biology and chemistry [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Various reviews summarize the application of ML/DL in drug design and discovery [ 18 , 19 , 20 , 21 , 22 ]. ML-based PLI prediction has been developed from a chemogenomic perspective [ 23 ] that considers interactions in a unified framework from chemical space and proteomic space.…”

Section: Introductionmentioning

confidence: 99%

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Verma

Trozzi

et al. 2021

IJMS

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Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

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“…9 Machine learning (ML) and Deep learning (DL) approaches have recently received attention in this field. Various reviews summarize the application of ML/DL in drug design and discovery [10][11][12][13][14] . Machine learning based PLI prediction has been developed from a chemogenomic perspective 15 that considers interactions in a unified framework from chemical space and proteomic space.…”

Section: Introductionmentioning

confidence: 99%

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Verma

Trozzi

et al. 2019

Preprint

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Computational prediction of bioactivity has become a critical aspect of modern drug discovery as it mitigates the cost, time, and resources required to find and screen new compounds. Deep Neural Networks (DNN) have recently shown excellent performance in modeling Protein-Ligand Interaction (PLI). However, DNNs are only effective when physically sound descriptions of ligands and proteins are fed into the network for further processing. Furthermore, previous research has not incorporated the secondary structure of the protein in a meaningful manner. In this work, we utilize secondary structure information of the protein which is extracted as the curvature and torsion of the backbone of protein to predict PLI. We demonstrate how our model outperforms previous machine and non-machine learning models on three major datasets: humans, C.elegans, and DUD-E. Visualization of the intermediate layers of our model shows a potential latent space for proteins which extracts important information about the activity of the protein. We further investigate the inner workings of our model by visualizing heatmaps through Grad-CAM. This analysis is adapted to visualize the most important aspects of the protein that the algorithm has learned. We observed that the important residues highlighted by Grad-CAM are the ones responsible for non-covalent interactions with a ligand and is not just confined to the binding site as it also includes allosteric sites and other locations where a ligand interacts. Our new model opens the door in exploration of DNN based on the secondary structure which is not just confined to protein ligand interactions. network for ligands Secondary structure based convolution neural network for proteinsCombined space

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Machine learning and artificial neural network accelerated computational discoveries in materials science

Cited by 77 publications

References 197 publications

Crystal-Site-Based Artificial Neural Networks for Material Classification

Crystal-Site-Based Artificial Neural Networks for Material Classification

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

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