Relevant bibliographies by topics / ADMET prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'ADMET prediction'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "ADMET prediction"

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Norinder, Ulf, and Christel A S. Bergström. "Prediction of ADMET Properties." ChemMedChem 1, no. 9 (2006): 920–37. http://dx.doi.org/10.1002/cmdc.200600155.

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Katole, Ravindra Madhukar, Mukesh Kumar Sharma, and Chetan Kumar Joshi. "PREDICTION OF BIOLOGICAL ACTIVITIES OF A. SQUAMOSA BIOACTIVE COMPOUNDS BY-SILICO DESIGN." Journal of Advanced Scientific Research 12, no. 03 (2021): 119–23. http://dx.doi.org/10.55218/jasr.202112316.

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Annona squamosa L. belongs to the Annonaceae family and is one of the basic dietary plants with edible fruits and is called "custard apple. The aim of our study was to carry out to PASS Predication investigate potential Prediction of Activity Spectra for phytoconstituents from Alpinia calcarata namely Acetogenins, Isoquinoline, Quercetin 3 Oglucoside, phenanthrene and Anoreticuin towards many deasease. Server based in silico pass prediction of the compounds was performed. Selected phytochemicals were also analyzed for ADME/T properties using the ADMET protocol. Among a wide range of predicting
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Bououden, Walid, and Yacine Benguerba. "Designing, Cytotoxic Evaluation, Molecular Docking and in Silico Pharmacokinetic Prediction of New Hydrocortisone Derivatives as Anti-Asthmatics Drugs." Journal of Drug Delivery and Therapeutics 10, no. 4 (2020): 8–16. http://dx.doi.org/10.22270/jddt.v10i4.4128.

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A series of new 20 corticosteroids were subjected to molecular property prediction. The Molecular, Physicochemical, and Biological properties were determined using Molinspiration Cheminformatics software. These compounds were further subjected to Toxicity Predictions using the Osiris Software. The calculated drug-related properties of the designed molecules were similar to those found in most marketed drugs. Amongst the proposed molecules, fourteen promising candidates can be considered as promising structures for the synthesis of new and more effective anti-asthmatic drugs. Result indicates t
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Dharani, J., and S. Ravi. "in silico ADMET Screening of Compounds Present in Cyanthillium cinereum (L.) H. Rob." Asian Journal of Chemistry 32, no. 6 (2020): 1421–26. http://dx.doi.org/10.14233/ajchem.2020.22569.

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Drug development involves assessment of absorption, distribution, metabolism, excretion and toxicity (ADMET) increasingly earlier in the discovery process. in silico ADMET studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, several in silico approaches for predicting ADMET properties of compounds from their chemical structure have been developed, ranging from data-based approaches. In this study, ADMET prediction has been done for 20 compounds from the plant Cyanthilli
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Dulsat, Júlia, Blanca López-Nieto, Roger Estrada-Tejedor, and José I. Borrell. "Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments." Molecules 28, no. 2 (2023): 776. http://dx.doi.org/10.3390/molecules28020776.

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For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski’s rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling.
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Wu, Jiaju, Linggang Kong, Ming Yi, et al. "Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification." Computational Intelligence and Neuroscience 2022 (July 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/4987639.

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Performance prediction based on candidates and screening based on predicted performance value are the core of product development. For example, the performance prediction and screening of equipment components and parts are an important guarantee for the reliability of equipment products. The prediction and screening of drug bioactivity value and performance are the keys to pharmaceutical product development. The main reasons for the failure of pharmaceutical discovery are the low bioactivity of the candidate compounds and the deficiencies in their efficacy and safety, which are related to the
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Vassiliev, P. M., A. V. Golubeva, A. R. Koroleva, M. A. Perfilev, and A. N. Kochetkov. "In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds." Safety and Risk of Pharmacotherapy 11, no. 4 (2023): 390–408. http://dx.doi.org/10.30895/2312-7821-2023-11-4-390-408.

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Scientific relevance. Studies of the toxicological and pharmacokinetic properties of medicinal compounds are a crucial stage of preclinical research; unsatisfactory results may invalidate further drug development. Therefore, the development of in silico methods for a preliminary pre-experimental assessment of toxicological and pharmacokinetic properties is a relevant and crucial task.Aim. The study aimed to review current approaches to in silico prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters of pharmacologically active compounds, in particula
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Monk, Alan J. "Faster, surer prediction: Harnessing automated microscopy for drug discovery." Biochemist 27, no. 5 (2005): 25–28. http://dx.doi.org/10.1042/bio02705025.

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As many as 60% of investigational new drugs fail in the later preclinical and clinical phases of development because of unsatisfactory absorption, distribution, metabolism, excretion and/or toxicity (ADMET) characteristics. There is therefore a great need for tools that can help to predict the ADMET response earlier in drug development, before valuable resources are wasted on compounds destined ultimately to fail. ADMET characteristics are reliant on many co-ordinated events and feedback pathways in the intact organism and cannot readily be measured accurately in in vitro biochemical assays. T
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van de Waterbeemd, Han, and Eric Gifford. "ADMET in silico modelling: towards prediction paradise?" Nature Reviews Drug Discovery 2, no. 3 (2003): 192–204. http://dx.doi.org/10.1038/nrd1032.

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Sumardi, Sumardi, Suprianto Suprianto, Suci Rahmadani Siregar, Arifin Putra Zai, Regina Elianda Tambubolon, and Fridelly Mairani. "ADMET Prediction Compounds of Polar Extract Curcuma rhizoma." Jurnal Indah Sains dan Klinis 5, no. 3 (2025): 01–08. https://doi.org/10.52622/jisk.v5i3.01.

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The pharmacokinetic and metabolic profiles of polar extract compounds from Curcuma rhizoma were evaluated using ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions. Most compounds exhibited high gastrointestinal (GI) absorption, with the exception of D-glucose, citric acid, and terpenoids such as alpha-pinene and zingiberene, which demonstrated low absorption, highlighting potential challenges for systemic bioavailability. Blood-brain barrier (BBB) permeability was observed in lipophilic compounds like xanthorrhizol, bisdemethoxycurcumin, and terpenoids, suggestin
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Dissertations / Theses on the topic "ADMET prediction"

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Piechota, Przemyslaw. "Development of in silico models for the prediction of toxicity incorporating ADME information." Thesis, Liverpool John Moores University, 2015. http://researchonline.ljmu.ac.uk/4554/.

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Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, t
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Hobbs, Michael. "The interplay of multiple ADME mechanisms : prediction of hepatic drug-drug interactions from in vitro." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27533.

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Rosuvastatin has poor passive membrane permeability and its uptake into the liver is mediated predominately by the transporter, Organic Anion Transporting Polypeptide 1B1 (OATP1B1). Cyclosporin A (CsA) is a potent inhibitor of a range of transporters, including OATP1B1 and clinical drug-drug interactions (DDI) have been reported with rosuvastatin. The aim of this study was to determine the uptake kinetics of rosuvastatin in human hepatocytes using a mechanistic model and to determine the inhibitory effect of CsA upon those kinetics. These data may then allow the extrapolation of in vitro to in
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Mazzolari, A. "IN SILICO APPROACHES IN DRUG DESIGN AND DEVELOPMENT: APPLICATIONS TO RATIONAL LIGAND DESIGN AND METABOLISM PREDICTION." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/347523.

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In the last decades, the applications of computational methods in medicinal chemistry have experienced significant changes which have incredibly expanded their approaches, and more importantly their objectives. The overall aim of the present research project is to explore the different fields of the modelling studies by using well-known computational methods as well as different and innovative techniques. Indeed, computational methods traditionally consisted in ligand-based and the structure-based approaches substantially aimed at optimizing the ligand structure in terms of affinity, potenc
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Shafi, Shanjeeda. "Machine learning and mixture clustering methods for molecular drug discovery: prediction and characterisation of drugs and druggable targets." Thesis, 2021. http://hdl.handle.net/1959.13/1431097.

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Research Doctorate - Doctor of Philosophy (PhD)<br>In the drug discovery process, approximately five to ten thousand compounds are initially screened but only 1% of these enter the preclinical testing stage that determines whether the compound is safe, efficacious, and feasible to use for a disease state. Owing to regulatory, toxicity, resistance and human health concerns, demand is increasing for refinement of and intensive use of molecular physicochemical properties via effective and robust mathematical methods for drug discovery. Chemoinformatics is now a well-recognised discipline focused
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Books on the topic "ADMET prediction"

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Wang, Jianling, and Laszlo Urban, eds. Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.

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Gabriele, Cruciani, ed. Molecular interaction fields: Applications in drug discovery and ADME prediction. Wiley-VCH, 2005.

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Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.

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HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should c
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Urban, Laszlo, and Jianling Wang. Predictive Admet: Integrated Approaches in Drug Discovery and Development. Wiley & Sons, Incorporated, John, 2014.

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Urban, Laszlo, and Jianling Wang. Predictive ADMET: Integrated Approaches in Drug Discovery and Development. Wiley & Sons, Incorporated, John, 2014.

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Urban, Laszlo, and Jianling Wang. Predictive ADMET: Integrated Approaches in Drug Discovery and Development. Wiley & Sons, Incorporated, John, 2014.

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Urban, Laszlo, and Jianling Wang. Predictive ADMET: Integrated Approaches in Drug Discovery and Development. Wiley & Sons, Incorporated, John, 2014.

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Urban, Laszlo, and Jianling Wang. Predictive ADMET: Integrated Approaches in Drug Discovery and Development. Wiley & Sons, Limited, John, 2014.

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Predictive Admet Integrative Approaches In Drug Discovery And Development. John Wiley & Sons Inc, 2014.

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Folkers, Gerd, Raimund Mannhold, Hugo Kubinyi, and Gabriele Cruciani. Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction. Wiley & Sons, Incorporated, John, 2006.

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Book chapters on the topic "ADMET prediction"

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Ronseaux, Sebastien, Jeremy Beck, and Clayton Springer. "Maximizing the Power of a Local Model for ADMET-Property Prediction." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch6.

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Fagerholm, Urban. "PK-Matrix-A Permeability: Intrinsic Clearance System for Prediction, Classification, and Profiling of Pharmacokinetics and Drug-Drug Interactions." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch5.

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Pozzan, Alfonso. "QM Calculations in ADMET Prediction." In Methods in Molecular Biology. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0282-9_18.

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Tarcsay, Ákos, and György M. Keserű. "ADMET Prediction Based on Protein Structures." In Protein Modelling. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09976-7_12.

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Du, Bing-Xue, Yi Xu, Siu-Ming Yiu, Hui Yu, and Jian-Yu Shi. "MTGL-ADMET: A Novel Multi-task Graph Learning Framework for ADMET Prediction Enhanced by Status-Theory and Maximum Flow." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29119-7_6.

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Verma, Neha, and Usha Chouhan. "ADMET Prediction of Dual PPARα/γ Agonists for Identification of Potential Anti-diabetic Agents." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0923-6_31.

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Faller, Bernard, Suzanne Skolnik, and Jianling Wang. "ADMET Diagnosis Models." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch3.

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Perkin, Valentin O., Grigory V. Antonyan, Eugene V. Radchenko, and Vladimir A. Palyulin. "Web Services for the Prediction of ADMET Parameters Relevant to the Design of Neuroprotective Drugs." In Neuromethods. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3311-3_16.

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Gao, Wentao, Ziyi Huang, Hao Zhang, and Jianfeng Lu. "Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_3.

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Urban, Laszlo, Jean-Pierre Valentin, Kenneth I. Kaitin, and Jianling Wang. "Current Social, Clinical, and Scientific Environment of Pharmaceutical R&D." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch1.

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Conference papers on the topic "ADMET prediction"

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Menon, Deepak, Achyut M. Sharma, and R. Ani. "Computational Approaches for Drug Discovery: A Review of Artificial Intelligence Techniques for ADME/T Prediction and Virtual Screening." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724127.

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Bhavitha, Ps Lekshmi Prasad, R. Ani, and Os Deepa. "Machine Learning Based ADMET Prediction in Drug Discovery." In 2023 4th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2023. http://dx.doi.org/10.1109/gcat59970.2023.10353396.

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Yang, Chaochao, Junjie Wang, Anjun Liu, Dan Wang, Jie Hou, and Meng Guo. "Prediction effect of ADMET properties based on machine learning." In ACM ICEA '21: 2021 ACM International Conference on Intelligent Computing and its Emerging Applications. ACM, 2021. http://dx.doi.org/10.1145/3491396.3506507.

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Carpencov, Serghei, and Veaceslav Kulcitki. "Docking and admet studies of manoiloxide derivatives." In Scientific seminar with international participation "New frontiers in natural product chemistry". Institute of Chemistry, Republic of Moldova, 2023. http://dx.doi.org/10.19261/nfnpc.2023.ab13.

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Manoiloxide is a labdane type diterpenoid that can be synthesized from commercially available sclareol. Derivatives of this substance contain the scaffold of forskolin 1 and potentially bind to the active site of adenylate cyclase (PDB 1ab8) showing cytotoxic and cytostatic activity. We report in the current communication the use of Autodock vina and Molegro Virtual Docker software packages for the prediction of the approximate values of binding affinity to the active site of this protein of a series of manoyloxide derivatives. The prediction of the probable adsorption, transportation and toxi
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Tey, Han Jieh, Chong Han Ng, and Thong Leng Lim. "Virtual Screening and ADMET Prediction of RanBP9 Inhibitors for Alzheimer’s Disease." In 2019 7th International Conference on Information and Communication Technology (ICoICT). IEEE, 2019. http://dx.doi.org/10.1109/icoict.2019.8835252.

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Xiao, Haoliang, and Xiangyang Chen. "Drug ADMET Prediction Method Based on Improved Graph Convolution Neural Network." In 2022 4th International Conference on Robotics and Computer Vision (ICRCV). IEEE, 2022. http://dx.doi.org/10.1109/icrcv55858.2022.9953254.

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Shin, Moonshik, Sungyoung Yoo, Suhyun Ha, Kyungrin Noh, and Doheon Lee. "Identifying Potential Bioactive Compounds of Natural Products by Combining ADMET Prediction Methods." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. ACM, 2015. http://dx.doi.org/10.1145/2811163.2811168.

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Wu, Chao, Zongxiang Huang, Yanghui Feng, Yukun Wang, Zongsheng Xie, and Jinsheng Chen. "Classification Prediction of ADMET Properties for Anti-Pancreatic Cancer Drug Candidates Using Machine Learning Approaches." In AIBDF 2024: The 4th Asia-Pacific Artificial Intelligence and Big Data Forum. ACM, 2024. https://doi.org/10.1145/3718491.3718639.

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El-Khouly, Omar A., Dina I. A. Othman, Amany S. Mostafa, and Mohammed A. M. Massoud. "Thiazolopyrimidine as a Promising Anticancer Pharmacophore: In Silico Drug Design, Molecular Docking and ADMET Prediction Studies." In ECMC 2022. MDPI, 2022. http://dx.doi.org/10.3390/ecmc2022-13313.

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Bouone, Yousra Ouafa, Abdeslem Bouzina, and Nour-Eddine Aouf. "Synthesis, Molecular Docking Analysis, ADMET and Drug Likeness Prediction of a Benzenesulfonamide Derivative Analogue of SLC-0111." In ECMC 2022. MDPI, 2022. http://dx.doi.org/10.3390/ecmc2022-13407.

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