A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

Al-Fakih, Abdo Mohammed; Algamal, Zakariya Yahya; Lee, Muhammad Hisyam; Aziz, Mohamad Saiful Islam; Ali, Haithem Taha Mohammad

doi:10.1080/1062936x.2019.1607899

Cited by 13 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test the predicting performance of our proposed algorithm, HHOA‐OBL, comprehensive comparative experiments with the original HHOA, grid search (GS) approach, and cross‐validation (CV) approach with 10 folds are utilized. Four different sets of chemical datasets were used in this research: antidiabetic activity of dipeptidyl peptidase‐IV inhibitors (Dataset 1), 63 influenza neuraminidase a/PR/8/34 (H1N1) inhibitors (Dataset 2), anticancer potency of imidazo[4,5‐b]pyridine derivatives (Dataset 3), 64 and diverse series of antifungal agents (Dataset 4) 65 . All these datasets include thousands of descriptors as features.…”

Section: Resultsmentioning

confidence: 99%

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Ismael

Qasim

Algamal

2020

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

Many real problems have been solved by support vector regression, especially v‐support vector regression (v‐SVR), but there are hyperparameters that usually needed to tune. In addition, v‐SVR cannot perform feature selection. Nature‐inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, the opposition‐based learning Harris hawks optimization algorithm (HHOA‐OBL) is proposed to optimize the hyperparameters of the v‐SVR with embedding the feature selection simultaneously. The experimental results over four datasets show that the HHOA‐OBL outperforms the standard Harris hawks optimization algorithm, grid search, and cross‐validation methods, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the HHOA‐OBL confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared with other nature‐inspired algorithms, which proves the ability of HHOA‐OBL in searching for the best hyperparameters values and selecting the most informative features for prediction tasks. Thus, the experiments and comparisons support the performance of the proposed approach in making predictions in other real applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Ismael

Qasim

Algamal

2020

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…This can be defined as a Quadratic Programming>problem, which is to find the minimum point of the equation. The formula to find the minimum point is written as equation ( 6) and (7) [10] [16].…”

Section: Support Vector Machinementioning

confidence: 99%

“…In 2009, Saqib and Mohammad conducted a QSAR 3D study on triazolopiperazine-amides inhibitors of dipeptidyl peptidase-IV as an anti-diabetic and obtained results of 𝑟 2 and 𝑟 𝑝𝑟𝑒𝑑 2 were 0.816 and 0.863 [6]. In 2019 Al-Fakih and Algamal conducted research on QSAR for the prediction of anti-diabetic activity using the binary gravitational search algorithm method and obtained TVBGSA results 𝑄 𝑖𝑛𝑡 2 of 0.957 and 𝑄 𝐿𝐺𝑂 2 of 0.951 [7].…”

Section: Introductionmentioning

confidence: 99%

QSAR Study on Diacylgycerol Acyltransferase-1 (DGAT-1) Inhibitor as Anti-diabetic using PSO-SVM Methods

Pramana

Septiawan

Kurniawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

Diabetes mellitus is a chronic disease that can occurred to anyone. Up until now, there are no specific drugs that have been found which can completely cure diabetes. One of the possible steps to treat diabetes mellitus is by inhibiting the growth of Diacylglycerol Acyltransferase-1 (DGAT-1) enzyme. This study aims to build a QSAR model on DGAT-1 inhibitors as anti-diabetic using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). Acyl-CoA: DGAT1 is a mikrosomal enzyme in lipogenesis which is increased in metabolically active cells to meet nutrient requirements. Microsomal-enzymes-that have an-important in the triglyceride-synthesis process-of 1,2-diacylglycerol by-catalyzing-acyl-coa-dependent-acylations as anti-diabetics. The dataset used in this study consists of 228 samples containing molecular structures and their inhibitor activities. We reduce the number of features by removing features with a standard deviation less than the threshold value, followed by the PSO algorithm. The best-predicted result is obtained through the implementation of SVM with RBF kernel, with the score of and are 0.75 and 0.67, respectively.

show abstract

“…Information on these databases, distinct and diverse structures, and the pharmaceutical importance of NPs was recently discussed. , The availability of data sets on antimalarial NPs and modern computational advances has motivated scientists to develop in silico frameworks to virtually identify potential NPs possessing strong antimalarial activity. The past two decades have seen an explosion of applications of ML and DL in various fields, especially central science − and life science. − The emergence of interdisciplinary fields, such as cheminformatics, bioinformatics, and health informatics, confirms the indispensable role of in silico advancement in supporting wet-lab research. In 2018, Egieyeh et al first introduced a computational model to identify NPs having antimalarial activities .…”

Section: Introductionmentioning

confidence: 99%

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers

Nguyen-Vo

Trinh

Nguyen

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synthesized chemicals, a number of natural compounds have also been proven to be as effective in their antimalarial properties. Besides experimental approaches to investigate antimalarial activities in natural products, computational methods have been developed with satisfactory outcomes obtained. In this study, we propose a novel molecular encoding scheme based on Bidirectional Encoder Representations from Transformers and used our pretrained encoding model called NPBERT with four machine learning algorithms, including k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), and Random Forest (RF), to develop various prediction models to identify antimalarial natural products. The results show that SVM models are the best-performing classifiers, followed by the XGB, k-NN, and RF models. Additionally, comparative analysis between our proposed molecular encoding scheme and existing state-of-the-art methods indicates that NPBERT is more effective compared to the others. Moreover, the deployment of transformers in constructing molecular encoders is not limited to this study but can be utilized for other biomedical applications.

show abstract

A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

Cited by 13 publications

References 35 publications

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

QSAR Study on Diacylgycerol Acyltransferase-1 (DGAT-1) Inhibitor as Anti-diabetic using PSO-SVM Methods

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers

Contact Info

Product

Resources

About