Deep belief networks using hybrid fingerprint feature for virtual screening of drug design

Fitriawan, Aries; Wasito, Ito; Syafiandini, Arida Ferti; Amien, Mukhlis; Yanuar, Arry

doi:10.1109/icacsis.2016.7872737

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…A method to understand protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information was proposed by: Misagh Naderi [7] in a graph-based approach to construct Target focused libraries for virtual screening. In the paper of Deep Belief Networks for Ligand-Based Virtual Screening of Drug Design by Aries Fitriawan [8] suggest about the virtual screening method in drug discovery the author talks about finding a new method for ligand-based virtual screening using machine learning technique here the classification has been done by using Deep Belief Networks (DBN) method which permit any inter-layer model of Restricted Boltzmann Machine (RBM) to receive a different depiction of the data from its output. Whereas the RBM is a simplification of the Boltzmann Machine models that have the energy formula of joint configuration.…”

Section: Review Of Literaturementioning

confidence: 99%

Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

Nair¹,

Bhaskaran²,

Arunjit³

2017

ijdd

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<p>In this paper we are sketching the chemical structure of suppressor drug for autism spectrum disorder using a computational tool. Here we are designing three molecular compounds like Fluoxetine, Risperidone, Melatonin. Structuring the suppressors, sketching the aromatization and bonding of the functional groups with the elements like Oxygen, Nitrogen, halogens. In our work we are using computational algorithm for drawing the structure of suppressor drug. In this paper we are mentioning the autism spectrum suppressor’s molecular formula as well as structural formula.</p>

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Section: Review Of Literaturementioning

confidence: 99%

Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

Nair¹,

Bhaskaran²,

Arunjit³

2017

ijdd

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“…Fitriawan et al [ 10 ] developed a deep learning classification model for a nicotinamide adenine dinucleotide (NAD) protein target problem and used PubChem fingerprints as a feature. Dhanda et al [ 11 ] used a combination of hybrid fingerprint models to develop a support vector machine (SVM) prediction for drug compounds.…”

Section: Introductionmentioning

confidence: 99%

“…The virtual screening process typically identifies the potential binding of structures to each other, for instance, a drug compound and its protein targets. Virtual screening is based on compound similarity or database docking [10]. However, cheminformatic studies have found that computer science approaches, such as pharmacophore analysis [9] and some machine learning techniques, help identify the interaction between a drug and its protein targets [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Erlina

Paramita

Kusuma

et al. 2022

BMC Complement Med Ther

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Background The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. Methods We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. Results From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. Conclusions Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

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“…Several studies used deep learning to predict this DTI (Fitriawan et al, 2016;Lee et al, 2019;Mei and Zhang, 2019;Sulistiawan et al, 2020;Sajadi et al, 2021). Lee et al (2019) proposed a deep learning-based prediction model capturing local residue patterns of proteins participating in DTIs.…”

Section: Introductionmentioning

confidence: 99%

Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm

et al. 2022

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Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug–target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain Angelica Sinensis, Citrus aurantium, Glycyrrhiza uralensis, and Mangifera indica. This approach is expected to be an alternative way to discover the Jamu formula more accurately.

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Deep belief networks using hybrid fingerprint feature for virtual screening of drug design

Cited by 9 publications

References 11 publications

Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm

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