Ligand Based Virtual Screening Using Self-organizing Maps

Jayaraj, P. B.; Sanjay, S S; Raja, Koustub; Gopakumar, G; Jaleel, U. C. Abdul

doi:10.1007/s10930-021-10030-9

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Virtual Screening includes many computational methods to screen a large number of small molecules in order to identify hit ligands in the drug discovery process [11]. The two crucial virtual screening approaches towards drug discovery are Ligand Based Virtual Screening (LBVS) and Structure Based Virtual Screening (SBVS) [12]- [15]. Many computational approaches are there to implement the two approaches.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Neural Network-Based Virtual Screening of Phytochemicals and In-Silico Docking Studies of Drug Compounds for Hemochromatosis

Ani,

Deepa

2023

IEEE Access

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Machine learning based Virtual Screening has proved as an important intermediate process that helps in the field of drug discovery in reducing the cost and manpower of classical drug discovery process. This work proposes a deep learning based virtual screening model for the early discovery of drug compounds for the disease named Hemochromatosis, which is the excess absorption of iron in the human body. Our study focuses on finding possible drug compounds from medicinal plants to cure Hemochromatosis. The proposed method uses Graph Convolutional Neural Networks (GCN) for Ligand Based Virtual Screening (LBVS). Deep Learning algorithm, GCN outperformed all other experimented models in LBVS with an accuracy of 98.26% and F-score of 98% respectively. A small set of biologically active compounds was identified from the phytochemical dataset after performing the LBVS. The selected ligands after LBVS are taken for In-Silico Structure Based Screening (SBVS) called molecular docking and the best compounds that have high binding affinity towards the disease protein for Hemochromatosis is selected and recommended for in-vitro studies. Ablation studies are done with 12 different machine learning models including ensemble models. The proposed model exhibited a related percentage improvement of around 0.5% in accuracy and F-score, when compared to the tree based ensemble model, XGBoost. This study aims to suggest in-silico studies for ligand based and structure based screening to identify potential drug molecules from medicinal plants which can be tested in in-vitro analysis and studies.

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Section: Introductionmentioning

confidence: 99%

Graph Convolutional Neural Network-Based Virtual Screening of Phytochemicals and In-Silico Docking Studies of Drug Compounds for Hemochromatosis

Ani,

Deepa

2023

IEEE Access

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FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening

Liu,

Chen,

et al. 2024

Computational Biology and Chemistry

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Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery

Pasrija

Jha

Upadhyaya

et al. 2022

CTMC

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Background: The lengthy and expensive process of developing a novel medicine often takes many years and entails a significant financial burden due to its poor success rate. Furthermore, the processing and analysis of quickly expanding massive data necessitate the use of cutting-edge methodologies. As a result, Artificial Intelligence-driven methods that have been shown to improve the efficiency and accuracy of drug discovery have grown in favor. Objective: The goal of this thorough analysis is to provide an overview of the drug discovery and development timeline, various approaches of drug design, and the use of Artificial Intelligence in many aspects of drug discovery. Methods: Traditional drug development approaches and their disadvantages are explored, followed by an introduction to AI-based technology. Also, advanced methods used in Machine Learning and Deep Learning are examined in detail. A few examples of big data research that has transformed the field of medication discovery have also been presented. Also covered are the many databases, toolkits, and software available for constructing Artificial Intelligence/Machine Learning models, as well as some standard model evaluation parameters. Finally, recent advances and uses of Machine Learning and Deep Learning in drug discovery are thoroughly examined, along with their limitations and future potential. Conclusion: Artificial Intelligence-based technologies enhance decision-making by utilizing the abundantly available high-quality data, and thereby, reduce the time and cost involved in the process. We anticipate that this review would be useful to researchers interested in Artificial Intelligence-based drug development.

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Ligand Based Virtual Screening Using Self-organizing Maps

Cited by 3 publications

References 23 publications

Graph Convolutional Neural Network-Based Virtual Screening of Phytochemicals and In-Silico Docking Studies of Drug Compounds for Hemochromatosis

Graph Convolutional Neural Network-Based Virtual Screening of Phytochemicals and In-Silico Docking Studies of Drug Compounds for Hemochromatosis

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening

Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery

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