Relevant bibliographies by topics / ADME Prediction

Contents

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

Academic literature on the topic 'ADME Prediction'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "ADME Prediction"

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Liu, Ke, Xiangyan Sun, Lei Jia, et al. "Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction." International Journal of Molecular Sciences 20, no. 14 (2019): 3389. http://dx.doi.org/10.3390/ijms20143389.

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Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-s
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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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Komura, Hiroshi, Reiko Watanabe, and Kenji Mizuguchi. "The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery." Pharmaceutics 15, no. 11 (2023): 2619. http://dx.doi.org/10.3390/pharmaceutics15112619.

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Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evol
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Khare, Soumya, Tanushree Chatterjee, Shailendra Gupta, and Patel Ashish. "Bioavailability predictions, pharmacokinetics and drug-likeness of bioactive compounds from Andrographis paniculata using Swiss ADME." MGM Journal of Medical Sciences 10, no. 4 (2023): 651–59. http://dx.doi.org/10.4103/mgmj.mgmj_245_23.

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Abstract Background: Earlier research on Andrographis paniculata focused on documenting their bioactive compounds profiles and traditional use. Before making a drug-like substance prediction using information from in silico experimental models, the current work aimed to examine and analyze the ADMET properties. This study assessed the drug-likeness and ADMET characteristics of bioactive compounds from A. paniculata. Materials and Methods: The current study will be the first to use the free online tool Swiss ADME to report the ADME characteristics of A. paniculata. The ADME properties of 10 bio
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Shao, Changheng, Fengjing Shao, Song Huang, Rencheng Sun, and Tao Zhang. "An Evolved Transformer Model for ADME/Tox Prediction." Electronics 13, no. 3 (2024): 624. http://dx.doi.org/10.3390/electronics13030624.

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Drug discovery aims to keep fueling new medicines to cure and palliate many ailments and some untreatable diseases that still afflict humanity. The ADME/Tox (absorption, distribution, metabolism, excretion/toxicity) properties of candidate drug molecules are key factors that determine the safety, uptake, elimination, metabolic behavior and effectiveness of drug research and development. The predictive technique of ADME/Tox drastically reduces the fraction of pharmaceutics-related failure in the early stages of drug development. Driven by the expectation of accelerated timelines, reduced costs
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Jyoti V. Mali, Jyoti V. Mali, Pratibha R. Adnaik Pratibha R. Adnaik, and Rahul S. Adnaik Rahul S. Adnaik. "Computational ADME Modeling of Selected Terpenoids Using Swiss ADME." International Journal of Pharmaceutical Research and Applications 10, no. 3 (2025): 1704–16. https://doi.org/10.35629/4494-100317041716.

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Terpenoids, a large and diverse class of naturally occurring organic compounds, have shown significant therapeutic potential, including antimicrobial, anticancer, and anti-inflammatory properties. However, the pharmacokinetic behavior of terpenoids, particularly their absorption, distribution, metabolism, and excretion (ADME) profiles, remains a critical factor in their drug development process. Computational ADME modeling has emerged as a powerful tool for earlystage screening of bioactive compounds, enabling the efficient prediction of pharmacokinetic properties and drug-likeness. This revie
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Bang, Dongmin, Juyeon Kim, Haerin Song, and Sun Kim. "ADME-drug-likeness: enriching molecular foundation models via pharmacokinetics-guided multi-task learning for drug-likeness prediction." Bioinformatics 41, Supplement_1 (2025): i352—i361. https://doi.org/10.1093/bioinformatics/btaf259.

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Abstract Summary Recent breakthroughs in AI-driven generative models enable the rapid design of extensive molecular libraries, creating an urgent need for fast and accurate drug-likeness evaluation. Traditional approaches, however, rely heavily on structural descriptors and overlook pharmacokinetic (PK) factors such as absorption, distribution, metabolism, and excretion (ADME). Furthermore, existing deep-learning models neglect the complex interdependencies among ADME tasks, which play a pivotal role in determining clinical viability. We introduce ADME-DL (drug likeness), a novel two-step pipe
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Boobis, Alan, Ursula Gundert-Remy, Pierre Kremers, Panos Macheras, and Olavi Pelkonen. "In silico prediction of ADME and pharmacokinetics." European Journal of Pharmaceutical Sciences 17, no. 4-5 (2002): 183–93. http://dx.doi.org/10.1016/s0928-0987(02)00185-9.

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Winkler, D. A. "Neural networks in ADME and toxicity prediction." Drugs of the Future 29, no. 10 (2004): 1043. http://dx.doi.org/10.1358/dof.2004.029.10.863395.

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Dissertations / Theses on the topic "ADME 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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Forel, Alexandre. "Distributed Model Predictive Operation Control of Interconnected Microgrids." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206145.

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The upward trends in renewable energy deployment in recent years brings new challengesto the development of electrical networks. Interconnected microgrids appear as a novelbottom-up approach to the production and integration of renewable energy.Using model predictive control (MPC), the energy management of several interconnectedmicrogrids is investigated. An optimisation problem is formulated and distributed ontothe individual units using the alternating direction method of multipliers (ADMM). Themicrogrids cooperate to reach a global optimum using neighbour-to-neighbour communications.The ben
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Gomes, Mireille. "Role of mutual information for predicting contact residues in proteins." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:5ec3c90c-73fb-494f-ad2e-efc718406aa4.

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Mutual Information (MI) based methods are used to predict contact residues within proteins and between interacting proteins. There have been many high impact papers citing the successful use of MI for determining contact residues in a particular protein of interest, or in certain types of proteins, such as homotrimers. In this dissertation we have carried out a systematic study to assess if this popularly employed contact prediction tool is useful on a global scale. After testing original MI and leading MI based methods on large, cross-species datasets we found that in general the performance
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Boussaid, Haithem. "Efficient inference and learning in graphical models for multi-organ shape segmentation." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0002/document.

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Cette thèse explore l’utilisation des modèles de contours déformables pour la segmentation basée sur la forme des images médicales. Nous apportons des contributions sur deux fronts: dans le problème de l’apprentissage statistique, où le modèle est formé à partir d’un ensemble d’images annotées, et le problème de l’inférence, dont le but est de segmenter une image étant donnée un modèle. Nous démontrons le mérite de nos techniques sur une grande base d’images à rayons X, où nous obtenons des améliorations systématiques et des accélérations par rapport à la méthode de l’état de l’art. Concernant
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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 "ADME Prediction"

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

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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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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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Molecular interaction fields: Applications in drug discovery and ADME prediction. Wiley-VCH, 2006.

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Folkers, Gerd, Raimund Mannhold, Hugo Kubinyi, and Gabriele Cruciani. Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction. Wiley-VCH Verlag GmbH, 2006.

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(Editor), Gabriele Cruciani, Raimund Mannhold (Series Editor), Hugo Kubinyi (Series Editor), and Gerd Folkers (Series Editor), eds. Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction (Methods and Principles in Medicinal Chemistry). Wiley-VCH, 2006.

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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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Book chapters on the topic "ADME Prediction"

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Yan, Aixia. "Prediction of ADME Properties." In Applied Chemoinformatics. Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527806539.ch6i.

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Siramshetty, Vishal B., Xin Xu, and Pranav Shah. "Artificial Intelligence in ADME Property Prediction." In Methods in Molecular Biology. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3441-7_17.

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Effinger, Angela, Caitriona M. O’Driscoll, Mark McAllister, and Nikoletta Fotaki. "In Vitro and In Silico ADME Prediction." In ADME Processes in Pharmaceutical Sciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99593-9_13.

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Effinger, Angela, Caitriona M. O´Driscoll, Mark McAllister, and Nikoletta Fotaki. "In Vitro and In Silico ADME Prediction." In ADME Processes in Pharmaceutical Sciences. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50419-8_15.

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Bell, Leslie, Suzanne Skolnik, and Dallas Bednarczyk. "Path (Probe ADME and Test Hypotheses): A Useful Approach Enabling Hypothesis-Driven ADME Optimization." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch4.

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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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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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Sugano, Kiyohiko. "Emerging Full Mechanistic Physiologically Based Modeling." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch10.

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Schroeder, Patricia. "Pharmacokinetic/Pharmacodynamic Modeling in Drug Discovery: A Translational Tool to Optimize Discovery Compounds Toward the Ideal Target-Specific Profile." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch11.

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Wagner, Christian, and Jennifer B. Dressman. "In Vitro-In SilicoTools to Predict Pharmacokinetics of Poorly Soluble Drug Compounds." In Predictive ADMET. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118783344.ch12.

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Conference papers on the topic "ADME 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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Li, Jiahui, Tianle Shen, Zekai Gu, et al. "ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction Under Uncertainties." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919893.

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Paul, Subho, Krishna Murari, Narayana Prasad Padhy, and Sukumar Kamalasadan. "Predictor-Corrector Accelerated ADMM Method for the SOCP-Relaxed AC-DC Optimal Power Flow." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688609.

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Gangarapu, Kiran, Julakanti Venu, Mulagada Monja, Thumma Gouthami, and Vasudha Bakshi. "Identification of degradation products of saquinavir mesylate by LC-MS: Molecular docking and in silico ADME prediction studies." In 4th International Electronic Conference on Medicinal Chemistry. MDPI, 2018. http://dx.doi.org/10.3390/ecmc-4-05627.

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Bešlo, Drago, Dejan Agić, Vesna Rastija, Maja Karnaš, Domagoj Šubarić, and Bono Lučić. "Analysis of prediction of water solubility and lipophilicity of coumarins by free cheminformatics tools." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.657d.

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Pharmacokinetics of drugs involves the study of the drug’s path through the body, from its introduction to its final excretion from the body (Absorption, Distribution, Metabolism, and Excretion (ADME)). The basis of pharmacokinetic tests provides insight into the behavior of the tested substance after it is introduced into the body. They include consideration of the physicochemical properties of the medicinal substance, as well as its biopharmaceutical properties. Experimental measurements are often demanding and take a long time to conclude the continuation of further research, whether it is
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Azzaoui, Khalil. "A Novel Approach to Prepare a Composite of Hydroxyapatite with Cellulose Nanocomposites by Novel Methods Including Theoretical Studies." In 8th World Conference on Chemistry and Chemical Engineering and 8th World Conference on Advanced Materials, Nanoscience and Nanotechnology. Eurasia Conferences, 2025. https://doi.org/10.62422/978-81-981865-7-7-022.

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This paper presents a novel cheminformatics approach for the design and synthesis of hydroxyapatite/ cellulose nanocomposites, which have potential biomedical and environmental applications, removal of dyes. The nanocomposites are synthesized by the co-precipitation method with different ratios of hydroxyapatite and cellulose. Over the past decade, calcium phosphate composites and similar biomaterials have seen commercial use in bone substitution and allograft applications. These biomaterial composites, which include an organic matrix and an inorganic mineral, have been developed. The principa
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Li, Weijian, Fengchong Lan, Jiqing Chen, and Yigang Li. "Capacity Characteristics Analysis and Remaining Useful Life Estimation Method of Ternary Lithium Battery Pack." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-adm-060.

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Objective: Lithium-ion batteries have been widely used in electric cars as the source of power with its advantages of high energy density. The development and use of electric vehicles are affected by the performance degradation of the battery with cycling and ageing. However, the present researches on the battery cycle life are mainly focused on the single cell rather than battery packs, which is different from the actual situation of electric vehicles provided energy by battery packs. This paper examines the degradation characteristics of the battery pack during the charge and discharge cycle
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Macha, Vaclav, Vaclav Smidl, and Zdenek Peroutka. "Implementation of predictive spectrum control of a LC filter using ADMM." In 2017 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE). IEEE, 2017. http://dx.doi.org/10.1109/precede.2017.8071272.

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Rey, Felix, Damian Frick, Alexander Domahidi, Juan Jerez, Manfred Morari, and John Lygeros. "ADMM prescaling for model predictive control." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798820.

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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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