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Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation

Chia-Ter Chao, You-Tien Tsai, Wen-Ting Lee, Hsiang-Yuan Yeh, Chih-Kang Chiang, Vladimir Jakovljevic
2022 Oxidative Medicine and Cellular Longevity  
A deep representation learning was done using a high-level description of the local network architecture and features of the entities, followed by learning the global embeddings of nodes derived from a  ...  Our algorithm conferred a good distinction between potential compounds, presenting as higher prediction scores for the compound categories with a higher potential but lower scores for other categories.  ...  Acknowledgments We are grateful to the Second Core Laboratory, Department of Medical Research of National Taiwan University Hospital and the National Taiwan University Center of Genomic and Precision Medicine  ... 
doi:10.1155/2022/4378413 pmid:35035662 pmcid:PMC8754599 fatcat:nhzleduxwzchxlgd5dxt746xym

Novel prediction methods for virtual drug screening [article]

Josip Mesarić
2022 arXiv   pre-print
Deep learning is to stay in drug discovery but has a long way to go.  ...  One of key parts of the early drug discovery process has become virtual drug screening -- a method used to narrow down search for potential drugs by running computer simulations of drug-target interactions  ...  MACHINE LEARNING APPROACHES Most machine learning approaches can on a high level be described through four steps: data cleaning and preprocessing, feature extraction, model fitting and evaluation of results  ... 
arXiv:2202.06635v1 fatcat:cab5pvnvw5httnuksmb4ke2piy

Machine learning of molecular electronic properties in chemical compound space

Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, O Anatole von Lilienfeld
2013 New Journal of Physics  
Here, we present a machine learning (ML) model, trained on a data base of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and  ...  For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost.  ...  This approach is based on a strict first principles view on chemical compound space [11] .  ... 
doi:10.1088/1367-2630/15/9/095003 fatcat:znkaplzvxrfq5abojv22j37cey

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam
2020 Biotechnology and Bioprocess Engineering  
This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization  ...  In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.  ...  (NRF-2017M3A9C 4092978) of the Ministry of Science, ICT.  ... 
doi:10.1007/s12257-020-0049-y pmid:33437151 pmcid:PMC7790479 fatcat:wqdmkkas2nb65gy3pymlgisuwi

Prologue: Deep Insights of Chemical Structures by Chemoinformatics Tools, Let's Think Forward! [chapter]

Amalia Stefaniu
2020 Cheminformatics and its Applications  
potential of the lead compound.  ...  Pharmacokinetics/ADMET properties such as absorption, distribution, metabolism, excretion and toxicity of designed structures are assessed through computational approaches too, aiming to predict the therapeutic  ... 
doi:10.5772/intechopen.91858 fatcat:7r3xkhxiebbd3l4fktldabniki

A Brief Review of Machine Learning-Based Bioactive Compound Research

Jihye Park, Bo Ram Beck, Hoo Hyun Kim, Sangbum Lee, Keunsoo Kang
2022 Applied Sciences  
This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds.  ...  Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related  ...  between structural properties and biological activities of chemical compounds, are used widely, along with the prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) [26]  ... 
doi:10.3390/app12062906 fatcat:5ootoacaxzeh7etmwk3gwvokci

Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network

Yeh, Chao, Lai, Chen
2020 International Journal of Environmental Research and Public Health  
A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical  ...  We investigate the powerful ability of deep learning approach to learn the proper molecular descriptors for Meridian prediction and to provide novel insights into the complementary and alternative medicine  ...  Conflicts of Interest: The authors declare no conflict of interest and the funders had no role in the design of the study.  ... 
doi:10.3390/ijerph17030740 pmid:31979314 pmcid:PMC7036907 fatcat:m67riaih6rawlkux62fxza6dfa

A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds

Sunyong Yoo, Hyung Chae Yang, Seongyeong Lee, Jaewook Shin, Seyoung Min, Eunjoo Lee, Minkeun Song, Doheon Lee
2020 Frontiers in Pharmacology  
Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data.  ...  When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.  ...  Deep Learning-Based Prediction of the Medicinal Uses of Natural Compounds In this study, we used a deep learning model to predict the potential medicinal effects of natural compounds (Figure 2 ).  ... 
doi:10.3389/fphar.2020.584875 pmid:33519445 pmcid:PMC7845697 fatcat:gxhzbswfbnfjnaclkciryqgmg4

A Survey on Drug-Target Interaction Prediction Methods Analysis of Prediction Mechanisms for Drug Target Discovery

Shyama M Nair
2018 International Journal for Research in Applied Science and Engineering Technology  
In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of drug and protein data.  ...  Therefore, there is a continuous demand for effective and low-cost computational techniques for drug target interaction prediction.  ...  For example, many in silico repositioning approaches search potential drugtarget interactions through chemical structure information.  ... 
doi:10.22214/ijraset.2018.3057 fatcat:s3qchrwn5fbv3e3bkt7nfjtaum

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

Manish Kumar Tripathi, Abhigyan Nath, Tej P. Singh, A. S. Ethayathulla, Punit Kaur
2021 Molecular diversity  
The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space  ...  The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design.  ...  a deep learning algorithm for compound identifica- tion https:// github. com/ deepc hem/ deepc hem [23] 6 DeepTox Predict the toxicity of chemical compounds using deep learning algorithm www  ... 
doi:10.1007/s11030-021-10256-w pmid:34159484 pmcid:PMC8219515 fatcat:p3lsp57x6rbnxgxdu7y5dggdeu


2022 Asian Journal of Pharmaceutical and Clinical Research  
now giving a cornerstone for the establishment of greater automation into detail of this process.  ...  This could likely speed up time duration for compound discovery and enhancement and authorize more productive hunts of related chemicals.  ...  predictions for the unseen classes of compounds.  ... 
doi:10.22159/ajpcr.2022.v15i4.43890 fatcat:ptfmpvfxuvdffijfzztko3hmpu

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery

Celio F. Lipinski, Vinicius G. Maltarollo, Patricia R. Oliveira, Alberico B. F. da Silva, Kathia Maria Honorio
2019 Frontiers in Robotics and AI  
A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data.  ...  In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data.  ...  ACKNOWLEDGMENTS The authors would like to thank FAPESP-IBM (2016/18840-3), FAPESP (2016/24524-7), Pró-Reitoria de Pesquisa-Universidade de São Paulo (USP), CNPq, and CAPES for funding.  ... 
doi:10.3389/frobt.2019.00108 pmid:33501123 pmcid:PMC7805776 fatcat:osh2gfsr5vfufhokxe4voxauhi

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Rohan Gupta, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K Ambasta, Pravir Kumar
2021 Molecular diversity  
Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development.  ...  Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field.  ...  Acknowledgements We would like to thank the senior management of Delhi Technological University for their constant support and guidance.  ... 
doi:10.1007/s11030-021-10217-3 pmid:33844136 pmcid:PMC8040371 fatcat:yltthjorrvfrjgyrnszpxgpb2q

Artificial Intelligence in Drug Design

Gerhard Hessler, Karl-Heinz Baringhaus
2018 Molecules  
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area.  ...  Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/molecules23102520 pmid:30279331 pmcid:PMC6222615 fatcat:gx6klyz3afhcbmxgynv2jn6zeq

Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents

Dimitrios Vlachakis, Panayiotis Vlamos
2021 Mathematics in Computer Science  
Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and  ...  In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2.  ...  Predicting such properties through deep neural networks is considered a case of supervised learning.  ... 
doi:10.1007/s11786-021-00517-0 fatcat:djpi4fgonrfwhlk4ushvfbwyze
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