State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang, Shan; Di, Jinwei; Wang, Dan; Dai, Xudong; Hua, Yabing; Gao, Xiang; Zheng, Aiping; Gao, Jing

doi:10.3390/pharmaceutics14010183

Cited by 33 publications

(15 citation statements)

References 163 publications

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“…Artificial Neural Network (ANN) merupakan salah satu metode terbaik dalam memprediksi, termasuk memprediksi kebutuhan obat [4]. Cara kerja yang dilakukan dengan mengadopsi jaringan syaraf pada ANN dapat dilakukan dengan beragam model seperti Multilayer Perceptron (MLP), Deep Neural Network (DNN), Generalized Regeression Neural Network (GRNN) atau Monotonic MLP [5], [6]. Namun Neural Network memiliki masalah dalam penentuan jumlah neuron dan hidden layer optimal pada model arsitekturnya [7].…”

Section: Pendahuluanunclassified

“…Modifikasi ANN sudah dilakukan oleh beragam peneliti dengan menggunakan algoritme tertentu untuk melakukan training data seperti algoritme Backpropagation [9], Particle Swarm Optimization [10], K-Means [6] dan lain-lain. Jumlah input layer, hidden layer dan output layer yang diterapkan pada riset terdahulu berbeda untuk masing-masing algoritme agar menghasilkan prediksi terbaik dari data yang diolah.…”

Section: Pendahuluanunclassified

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Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

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Section: Pendahuluanunclassified

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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“…Weights are added for the connection of the neurons [29,30]. The ANN is trained by showing examples and modifying the weight values of the network according to specified learning rules until the ANN output matches the intended result [31]. A popular ANN is the Multilayer Perceptron (MLP) and it is made up of neurons termed perceptron [32].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

Adeniyi

Adeniyi²,

Oguns

et al. 2022

paradigmplus

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HR's purpose is to assign the best people to the right job at the right time, train and qualify them, and provide evaluation methods to track their performance and safeguard employees' perspective skills. These data are crucial for decision-makers, but collecting the best and most useful information from such large amounts of data is tough. HR employees no longer need to manually handle vast amounts of data with the advent of data mining. The basic purpose of data mining is to extract information from hidden patterns and trends in data to get near-optimal results. This study aims at comparing the performance of three techniques in the prediction of performance. The dataset undergoes preprocessing steps that include data cleaning, and data compression using PCA. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm. The result showed that Artificial Neural networks performed the best for the prediction of employee performance.

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“…ML and DL have accelerated the search for potent compounds with desired properties. , ML has significantly impacted kinase drug design as evidenced by the discovery of dual fibroblast growth factor receptor and epidermal growth factor receptor inhibitors . Commonly used ML algorithms, such as random forest (RF), extreme gradient boosting (XGB), or multilayer perceptron (MLP), are widely employed in predicting drug bioactivity and pharmaceutical compositions. − However, one limitation of conventional ML models is a high generalization error, as predictions rely on a single model output. In contrast, stacked generalization refers to any scheme that feeds information from one set of estimators to another before the final estimator, which reduces generalization error .…”

Section: Introductionmentioning

confidence: 99%

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

Syahid,

Weerapreeyakul,

Srisongkram

2023

ACS Omega

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The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC50) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination (R 2 and Q 2) than the individual baseline models. The stacking ensemble learning model provides good y-randomization results, indicating a strong correlation between molecular features and pIC50. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.

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State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Cited by 33 publications

References 163 publications

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

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