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Test No. 493: Performance-Based Test Guideline for Human Recombinant Estrogen Receptor (hrER) In Vitro Assays to Detect Chemicals with ER Binding Affinity [electronic resource] / Organisation for Econ...
Author / Creator:Organisation for Economic Co-operation and Development.
Publisher:Paris : OECD Publishing, 2015.
Date:2015.
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Lectins and pathology / edited by Michel Caron and Annie-Pierre Sève.
Publisher:Australia : Harwood Academic, c2000.
Call Numbers:N616.07/20
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ArticlesPeer ReviewedFull Text
LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone
Author / Creator:Chen, Peng ; Huang, Jianhua Z ; Gao, Xin
Publisher:England: BioMed Central
Journal title:BMC bioinformatics, 2014, Vol.15 (S15), p.S4-S4, Article S4
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ArticlesPeer ReviewedFull Text
Protein–ligand binding with the coarse-grained Martini model
Author / Creator:Souza, Paulo C. T. ; Thallmair, Sebastian ; Conflitti, Paolo ; Ramírez-Palacios, Carlos ; Alessandri, Riccardo ; Raniolo, Stefano ; Limongelli, Vittorio ; Marrink, Siewert J.
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2020-07, Vol.11 (1), p.1-11, Article 3714
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ArticlesPeer ReviewedFull Text
DeepDTAF: a deep learning method to predict protein–ligand binding affinity
Author / Creator:Wang, Kaili ; Zhou, Renyi ; Li, Yaohang ; Li, Min
Publisher:England
Journal title:Briefings in bioinformatics, 2021-09, Vol.22 (5)
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ArticlesPeer ReviewedFull Text
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
Author / Creator:Rube, H. Tomas ; Rastogi, Chaitanya ; Feng, Siqian ; Kribelbauer, Judith F. ; Li, Allyson ; Becerra, Basheer ; Melo, Lucas A. N. ; Do, Bach Viet ; Li, Xiaoting ; Adam, Hammaad H. ; Shah, Neel H. ; Mann, Richard S. ; Bussemaker, Harmen J.
Publisher:New York: Nature Publishing Group US
Journal title:Nature biotechnology, 2022-10, Vol.40 (10), p.1520-1527
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ArticlesPeer ReviewedFull Text
Prediction of protein–ligand binding affinity via deep learning models
Author / Creator:Wang, Huiwen
Publisher:England: Oxford University Press
Journal title:Briefings in bioinformatics, 2024-01, Vol.25 (2)
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ArticlesPeer ReviewedFull Text
Protonation and pK changes in protein–ligand binding
Author / Creator:Onufriev, Alexey V. ; Alexov, Emil
Publisher:New York, USA: Cambridge University Press
Journal title:Quarterly reviews of biophysics, 2013-05, Vol.46 (2), p.181-209
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ArticlesPeer ReviewedFull Text
A reinforcement learning approach for protein–ligand binding pose prediction
Author / Creator:Wang, Chenran ; Chen, Yang ; Zhang, Yuan ; Li, Keqiao ; Lin, Menghan ; Pan, Feng ; Wu, Wei ; Zhang, Jinfeng
Publisher:London: BioMed Central Ltd
Journal title:BMC bioinformatics, 2022-09, Vol.23 (1), p.1-368, Article 368
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ArticlesPeer ReviewedFull Text
A point cloud-based deep learning strategy for protein–ligand binding affinity prediction
Author / Creator:Wang, Yeji ; Wu, Shuo ; Duan, Yanwen ; Huang, Yong
Publisher:England: Oxford University Press
Journal title:Briefings in bioinformatics, 2022-01, Vol.23 (1)
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ArticlesPeer ReviewedFull Text
Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction
Author / Creator:Liu, Xiang ; Feng, Huitao ; Wu, Jie ; Xia, Kelin
Publisher:United States: Public Library of Science
Journal title:PLoS computational biology, 2022-04, Vol.18 (4), p.e1009943-e1009943
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ArticlesPeer ReviewedFull Text
Improving detection of protein-ligand binding sites with 3D segmentation
Author / Creator:Stepniewska-Dziubinska, Marta M. ; Zielenkiewicz, Piotr ; Siedlecki, Pawel
Publisher:London: Nature Publishing Group UK
Journal title:Scientific reports, 2020-03, Vol.10 (1), p.5035, Article 5035
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ArticlesPeer ReviewedFull Text
Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes
Author / Creator:Schiebel, Johannes ; Gaspari, Roberto ; Wulsdorf, Tobias ; Ngo, Khang ; Sohn, Christian ; Schrader, Tobias E. ; Cavalli, Andrea ; Ostermann, Andreas ; Heine, Andreas ; Klebe, Gerhard
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2018-09, Vol.9 (1), p.3559-15, Article 3559
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ArticlesPeer ReviewedFull Text
Improving the generalizability of protein-ligand binding predictions with AI-Bind
Author / Creator:Chatterjee, Ayan ; Walters, Robin ; Shafi, Zohair ; Ahmed, Omair Shafi ; Sebek, Michael ; Gysi, Deisy ; Yu, Rose ; Eliassi-Rad, Tina ; Barabási, Albert-László ; Menichetti, Giulia
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2023-04, Vol.14 (1), p.1989-15, Article 1989
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ArticlesPeer ReviewedFull Text
Terahertz underdamped vibrational motion governs protein-ligand binding in solution
Author / Creator:Turton, David A. ; Senn, Hans Martin ; Harwood, Thomas ; Lapthorn, Adrian J. ; Ellis, Elizabeth M. ; Wynne, Klaas
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2014-06, Vol.5 (1), p.3999-3999
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ArticlesPeer ReviewedFull Text
Predicting protein-ligand binding residues with deep convolutional neural networks
Author / Creator:Cui, Yifeng ; Dong, Qiwen ; Hong, Daocheng ; Wang, Xikun
Publisher:England: BioMed Central Ltd
Journal title:BMC bioinformatics, 2019-02, Vol.20 (1), p.93-93, Article 93
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ArticlesPeer ReviewedFull Text
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
Author / Creator:Pecina, Adam ; Fanfrlík, Jindřich ; Lepšík, Martin ; Řezáč, Jan
Publisher:London: Nature Publishing Group UK
Journal title:Nature communications, 2024-02, Vol.15 (1), p.1127-1127, Article 1127
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ArticlesPeer ReviewedFull Text
Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities
Author / Creator:Son, Jeongtae ; Kim, Dongsup
Publisher:United States: Public Library of Science
Journal title:PloS one, 2021-04, Vol.16 (4), p.e0249404
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ArticlesPeer ReviewedFull Text
On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities
Author / Creator:Wang, Lingle ; Berne, B. J. ; Friesner, Richard A.
Publisher:United States: National Academy of Sciences
Journal title:Proceedings of the National Academy of Sciences - PNAS, 2012-02, Vol.109 (6), p.1937-1942
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ArticlesPeer ReviewedFull Text
Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2
Author / Creator:Kurkcuoglu, Zeynep ; Koukos, Panagiotis I. ; Citro, Nevia ; Trellet, Mikael E. ; Rodrigues, J. P. G. L. M. ; Moreira, Irina S. ; Roel-Touris, Jorge ; Melquiond, Adrien S. J. ; Geng, Cunliang ; Schaarschmidt, Jörg ; Xue, Li C. ; Vangone, Anna ; Bonvin, A. M. J. J.
Publisher:Cham: Springer International Publishing
Journal title:Journal of computer-aided molecular design, 2018-01, Vol.32 (1), p.175-185
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Test No. 493: Performance-Based Test Guideline for Human Recombinant Estrogen Receptor (hrER) In Vitro Assays to Detect Chemicals with ER Binding Affinity [electronic resource] / Organisation for Econ...

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Lectins and pathology / edited by Michel Caron and Annie-Pierre Sève.

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LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

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Protein–ligand binding with the coarse-grained Martini model

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DeepDTAF: a deep learning method to predict protein–ligand binding affinity

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Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning

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Prediction of protein–ligand binding affinity via deep learning models

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Protonation and pK changes in protein–ligand binding

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A reinforcement learning approach for protein–ligand binding pose prediction

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A point cloud-based deep learning strategy for protein–ligand binding affinity prediction

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Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction

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Improving detection of protein-ligand binding sites with 3D segmentation

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Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes

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Improving the generalizability of protein-ligand binding predictions with AI-Bind

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Terahertz underdamped vibrational motion governs protein-ligand binding in solution

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Predicting protein-ligand binding residues with deep convolutional neural networks

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SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes

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Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

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On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities

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Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

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