Binding MOAD (Mother Of All Databases)

Hu, Liegi, Benson, Mark L., Smith, Richard D., Lerner, Michael G., Carlson, Heather A.·2005·Proteins: Structure, Function, and Bioinformatics
DOI 10.1002/prot.20512

Cited by (329)

Items 101–200 of 329

  1. Comparative evaluation of methods for the prediction of protein–ligand binding sites
    Javier S. Utgés, Geoffrey J. Barton · 2024 · Journal of Cheminformatics
  2. Structural Artifacts in Protein−Ligand X-ray Structures: Implications for the Development of Docking Scoring Functions
    Chresten R. Søndergaard, Alison Elizabeth Garrett, Tommy Carstensen, Gianluca Pollastri, Jens Erik Nielsen · 2009 · Journal of Medicinal Chemistry
  3. SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation
    Miles McGibbon, Sam Money-Kyrle, Vincent Blay, Douglas R. Houston · 2022 · Journal of Advanced Research
  4. Structure-Based Drug Design with a Deep Hierarchical Generative Model
    Weller, Jesse A., Rohs, Remo · 2024 · Journal of Chemical Information and Modeling
  5. Dock around the Clock – Current Status of Small Molecule Docking and Scoring
    Ulrich Rester · 2006 · QSAR & Combinatorial Science
  6. Differences between High- and Low-Affinity Complexes of Enzymes and Nonenzymes
    Heather A. Carlson, Richard D. Smith, Nickolay A. Khazanov, Paul D. Kirchhoff, James B. Dunbar, Mark L. Benson · 2008 · Journal of Medicinal Chemistry
  7. Combining Solvent Thermodynamic Profiles with Functionality Maps of the Hsp90 Binding Site to Predict the Displacement of Water Molecules
    Kamran Haider, David J. Huggins · 2013 · Journal of Chemical Information and Modeling
  8. Relationship between Enzyme/Substrate Properties and Enzyme Efficiency in Hydrolases
    Sérgio Filipe Sousa, Maria João Ramos, Carmay Lim, Pedro Alexandrino Fernandes · 2015 · ACS Catalysis
  9. GlycoTorch Vina: Docking Designed and Tested for Glycosaminoglycans
    Eric D. Boittier, Jed M. Burns, Neha S. Gandhi, Vito Ferro · 2020 · Journal of Chemical Information and Modeling
  10. Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level
    Fen Ma, Siwei Zhang, Lianhao Song, Bozhi Wang, Lanlan Wei, Fengmin Zhang · 2021 · Cell & Bioscience
  11. DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization
    Harrison Green, Jacob D. Durrant · 2021 · Journal of Chemical Information and Modeling
  12. Effect of ligand torsion number on the AutoDock mediated prediction of protein-ligand binding affinity
    Dinesh Kumar Sriramulu, Sangwook Wu, Sun‐Gu Lee · 2019 · Journal of Industrial and Engineering Chemistry
  13. Energy Landscape of Chelated Uranyl: Antibody Interactions by Dynamic Force Spectroscopy
    Michaël Odorico, Jean‐Marie Teulon, Thérèse Bessou, Claude Vidaud, Laurent Bellanger, Shu‐wen W. Chen, Éric Quéméneur, Pierre H. Parot, Jean‐Luc Pellequer · 2007 · Biophysical Journal
  14. Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
    Tobias Harren, Torben Gutermuth, Christoph Grebner, Gerhard Heßler, Matthias Rarey · 2024 · Wiley Interdisciplinary Reviews Computational Molecular Science
  15. Scoring Functions for AutoDock
    Anthony D. Hill, Peter J. Reilly · 2015 · Methods in molecular biology
  16. Electrostatic Energy in Protein–Ligand Complexes
    Gabriela Bitencourt‐Ferreira, Martina Veit‐Acosta, Walter Filgueira de Azevedo · 2019 · Methods in molecular biology
  17. Deep Confident Steps to New Pockets: Strategies for Docking Generalization
    Gabriele Corso, Arthur Deng, Benjamin N. Fry, Nicholas F. Polizzi, Regina Barzilay, Tommi Jaakkola · 2024 · PubMed
  18. Quantitative metrics for drug–target ligandability
    Siniša Vukovič, David J. Huggins · 2018 · Drug Discovery Today
  19. Structural Properties of Non-Traditional Drug Targets Present New Challenges for Virtual Screening
    Ragul Gowthaman, Eric J. Deeds, John Karanicolas · 2013 · Journal of Chemical Information and Modeling
  20. Docking to heme proteins
    Ute F. Röhrig, Aurélien Grosdidier, Vincent Zoete, Olivier Alain Michielin · 2009 · Journal of Computational Chemistry
  21. Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands
    Jacob D. Durrant, Kathryn E. Carlson, Teresa A. Martin, Tavina L. Offutt, Christopher G. Mayne, John A. Katzenellenbogen, Rommie E. Amaro · 2015 · Journal of Chemical Information and Modeling
  22. Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space
    Helena Strömbergsson, Paweł Daniluk, Andriy Kryshtafovych, Krzysztof A. Fidelis, Jarl E. S. Wikberg, Gerard J. Kleywegt, Torgeir Rhoden Hvidsten · 2008 · Journal of Chemical Information and Modeling
  23. Binding similarity network of ligand
    Keunwan Park, Dongsup Kim · 2007 · Proteins Structure Function and Bioinformatics
  24. Strain energy in enzyme–substrate binding: An energetic insight into the flexibility versus rigidity of enzyme active site
    Xinchun Guo, Deyong He, Ling Huang, Limin Liu, Lijun Liu, Huijuan Yang · 2012 · Computational and Theoretical Chemistry
  25. How Docking Programs Work
    Gabriela Bitencourt‐Ferreira, Walter Filgueira de Azevedo · 2019 · Methods in molecular biology
  26. PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks
    Alejandro Varela‐Rial, Iain Maryanow, Maciej Majewski, Stefan Doerr, Nikolai Schapin, José Jiménez-Luna, Gianni De Fabritiis · 2022 · Journal of Chemical Information and Modeling
  27. Text Mining Improves Prediction of Protein Functional Sites
    Karin M. Verspoor, Judith D. Cohn, Komandur Elayavilli Ravikumar, Michael E. Wall · 2012 · PLoS ONE
  28. Empirical Scoring Functions for Affinity Prediction of Protein‐ligand Complexes
    Lukas P. Pason, Christoph Sotriffer · 2016 · Molecular Informatics
  29. The Change of Protein Intradomain Mobility on Ligand Binding: Is It a Commonly Observed Phenomenon?
    Semen O. Yesylevskyy, Valery N. Kharkyanen, Alexander P. Demchenko · 2006 · Biophysical Journal
  30. Efficient generation of protein pockets with PocketGen
    Zhang, Zaixi, Shen, Wan Xiang, Liu, Qi, Zitnik, Marinka · 2024 · Nature Machine Intelligence
  31. Protein ligand interaction database (PLID)
    A. Srinivas Reddy, H.S. Durga Amarnath, Raju Surampudi Bapi, G. Madhavi Sastry, G. Narahari Sastry · 2008 · Computational Biology and Chemistry
  32. Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures
    Anna Carbery, Martin Buttenschoen, R. Skyner, F. von Delft, Charlotte Mary Deane · 2024 · Journal of Cheminformatics
  33. A geometry-based generic predictor for catalytic and allosteric sites
    Simon Mitternacht, Igor N. Berezovsky · 2010 · Protein Engineering Design and Selection
  34. New computational protein design methods for de novo small molecule binding sites
    James E. Lucas, Tanja Kortemme · 2020 · PLoS Computational Biology
  35. Use of QM/MM scheme to reproduce macromolecule–small molecule noncovalent binding energy
    Xinchun Guo, Deyong He, Limin Liu, Ren‐Yun Kuang, Lijun Liu · 2012 · Computational and Theoretical Chemistry
  36. A Prospective Cross-Screening Study on G-Protein-Coupled Receptors: Lessons Learned in Virtual Compound Library Design
    Marijn P. A. Sanders, Luc Roumen, Eelke van der Horst, J. Robert Lane, Henry F. Vischer, Jody van Offenbeek, Henk de Vries, Stefan Verhoeven, Ken Y.C. Chow, Folkert Verkaar et al. · 2012 · Journal of Medicinal Chemistry
  37. Beyond sequence: Structure-based machine learning
    Janani Durairaj, Dick de Ridder, Aalt D. J. van Dijk · 2022 · Computational and Structural Biotechnology Journal
  38. Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors
    Christian Krämer, Peter Gedeck · 2011 · Journal of Chemical Information and Modeling
  39. Ligand Identification Scoring Algorithm (LISA)
    Zheng Zheng, Kenneth M. Merz · 2011 · Journal of Chemical Information and Modeling
  40. A Gibbs free energy correlation for automated docking of carbohydrates
    Anthony D. Hill, Peter J. Reilly · 2007 · Journal of Computational Chemistry
  41. CSAR Scoring Challenge Reveals the Need for New Concepts in Estimating Protein–Ligand Binding Affinity
    Fedor N. Novikov, Alexey A. Zeifman, Oleg V. Stroganov, Viktor S. Stroylov, Val Kulkov, Ghermes G. Chilov · 2011 · Journal of Chemical Information and Modeling
  42. Surface‐based protein binding pocket similarity
    Russell Spitzer, Ann E. Cleves, Ajay N. Jain · 2011 · Proteins Structure Function and Bioinformatics
  43. SAnDReS: A Computational Tool for Docking
    Gabriela Bitencourt‐Ferreira, Walter Filgueira de Azevedo · 2019 · Methods in molecular biology
  44. PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications
    Divya B. Korlepara, C. S. Vasavi, Shruti Jeurkar, Pradeep Kumar Pal, Subhajit Roy, Sarvesh Mehta, Shubham Sharma, Vishal Kumar, Charuvaka Muvva, Bhuvanesh Sridharan et al. · 2022 · Scientific Data
  45. Language models for the prediction of SARS-CoV-2 inhibitors
    Blanchard, Andrew E, Gounley, John, Bhowmik, Debsindhu, Chandra Shekar, Mayanka, Lyngaas, Isaac, Gao, Shang, Yin, Junqi, Tsaris, Aristeidis, Wang, Feiyi, Glaser, Jens · 2022 · The International Journal of High Performance Computing Applications
  46. Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research
    C. George Priya Doss, Chiranjib Chakraborty, Vaishnavi Narayan, D. Thirumal Kumar · 2014 · Advances in protein chemistry and structural biology
  47. Analyzing Multitarget Activity Landscapes Using Protein–Ligand Interaction Fingerprints: Interaction Cliffs
    Oscar Méndez‐Lucio, Albert J. Kooistra, Chris de Graaf, Andreas Bender, José L. Medina‐Franco · 2015 · Journal of Chemical Information and Modeling
  48. Chemogenomic Analysis of G-Protein Coupled Receptors and Their Ligands Deciphers Locks and Keys Governing Diverse Aspects of Signalling
    Joerg Wichard, Antonius ter Laak, Gerd Krause, Nikolaus Heinrich, Ronald Kühne, Gunnar Kleinau · 2011 · PLoS ONE
  49. A High-Quality Data Set of Protein–Ligand Binding Interactions Via Comparative Complex Structure Modeling
    Xuelian Li, Cheng Shen, Hui Zhu, Yujian Yang, Qing Wang, Jincai Yang, Niu Huang · 2024 · Journal of Chemical Information and Modeling
  50. A workflow to create a high-quality protein–ligand binding dataset for training, validation, and prediction tasks
    Yingze Wang, Kunyang Sun, Jie Li, Xingyi Guan, Oufan Zhang, Dorian Bagni, Yang Zhang, Heather A. Carlson, Teresa L. Head-Gordon · 2025 · Digital Discovery
  51. A reinforcement learning approach for protein–ligand binding pose prediction
    Chenran Wang, Yang Chen, Yuan Zhang, Keqiao Li, Menghan Lin, Feng Pan, Wei Wu, Jinfeng Zhang · 2022 · BMC Bioinformatics
  52. Ligandability and druggability assessment via machine learning
    Francesco Di Palma, Carlo Abate, Sergio Decherchi, Andrea Cavalli · 2023 · Wiley Interdisciplinary Reviews Computational Molecular Science
  53. Automating human intuition for protein design
    Lucas G. Nivón, Sinisa Bjelic, Chris King, David A. Baker · 2013 · Proteins Structure Function and Bioinformatics
  54. Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity
    Debby D. Wang, Haoran Xie, Hong Yan · 2021 · Bioinformatics
  55. Guided multi-objective generative AI to enhance structure-based drug design
    Amit Kadan, Kevin Ryczko, Erika Lloyd, Adrián E. Roitberg, Takeshi Yamazaki · 2025 · Chemical Science
  56. Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates
    Jonathan D. Tyzack, Laurent Fernando, António J. M. Ribeiro, Neera Borkakoti, Janet M. Thornton · 2018 · Structure
  57. Navigating the Computational Seas: A Comprehensive Overview of Molecular Docking Software in Drug Discovery
    Mohd Mursal, Mohammad Nazir Ahmad, Sahil Hussain, Mohemmed Faraz Khan · 2024 · Biomedical engineering
  58. Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge
    Zhaofeng Ye, Matthew P. Baumgartner, Bentley M. Wingert, Carlos Jaime Camacho · 2016 · Journal of Computer-Aided Molecular Design
  59. One Drug for Multiple Targets: A Computational Perspective
    Oscar Méndez‐Lucio, J. Jesús Naveja, Hugo Vite‐Caritino, Fernando Daniel Prieto-Martínez, José L. Medina‐Franco · 2017 · Journal of the Mexican Chemical Society
  60. Machine learning optimization of cross docking accuracy
    Esben Jannik Bjerrum · 2016 · Computational Biology and Chemistry
  61. Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction
    Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, Gengmo Zhou · 2025 · Lecture notes in computer science
  62. Discovery and artificial intelligence-guided mechanistic elucidation of a narrow-spectrum antibiotic
    Denise B. Catacutan, Vian Tran, Autumn Arnold, Jeremie Alexander, Gabriele Corso, Yeganeh Yousefi, Megan M. Tu, Stewart McLellan, Dominique Tertigas, Kimberly Corneil et al. · 2025 · Nature Microbiology
  63. Identification of Mycobacterium tuberculosis enoyl-acyl carrier protein reductase inhibitors: A combined in-silico and in-vitro analysis
    Mohsin Yousuf Lone, Mohd Athar, Vivek Kumar Gupta, Prakash Chandra Jha · 2017 · Journal of Molecular Graphics and Modelling
  64. Biological perspective of thiazolide derivatives against Mpro and MTase of SARS-CoV-2: Molecular docking, DFT and MD simulation investigations
    Nouman Rasool, Farkhanda Yasmin, Shalini Sahai, Waqar Hussain, Hadiqa Inam, Arooj Arshad · 2021 · Chemical Physics Letters
  65. Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools
    Swapnil Wagle, Richard D. Smith, Anthony J. Dominic, Debarati DasGupta, Sunil Kumar Tripathi, Heather A. Carlson · 2023 · Scientific Reports
  66. MolSnapper: Conditioning Diffusion for Structure-Based Drug Design
    Yael Ziv, Fergus Imrie, Brian D. Marsden, Charlotte Mary Deane · 2025 · Journal of Chemical Information and Modeling
  67. Computational drug design in the artificial intelligence era: A systematic review of molecular representations, generative architectures, and performance assessment
    Karim Abbasi, Parvin Razzaghi, Ali Gharizadeh, Amin Ghareyazi, Abbas Dehnad, Hamid Reza Rabiee, Mohammad R. K. Mofrad · 2025 · Pharmacological Reviews
  68. Crystallizing new approaches for antimicrobial drug discovery
    Molly B. Schmid · 2006 · Biochemical Pharmacology
  69. Leveraging decagonal in-silico strategies for uncovering IL-6 inhibitors with precision
    Akey Krishna Swaroop, P. K. Krishnan Namboori, M. Esakkimuthukumar, T. Praveen, P. Nagarjuna, S. K. Patnaik, J. Selvaraj · 2023 · Comput. Biol. Medicine
  70. Equivariant 3D-Conditional Diffusion Model for De Novo Drug Design
    Zheng Jia, Hai-Cheng Yi, Zhu‐Hong You · 2024 · IEEE Journal of Biomedical and Health Informatics
  71. Simple Structure-Based Approach for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease
    Anthony F. Nastase, Donald B. Boyd · 2012 · Journal of Chemical Information and Modeling
  72. MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery
    2023
  73. A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design
    Lucian Chan, Rajendra Kumar, Marcel L. Verdonk, Carl R. Poelking · 2022 · Nature Machine Intelligence
  74. The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks
    Pierre-Yves Libouban, Samia Aci‐Sèche, José Carlos Gómez-Tamayo, Gary Tresadern, Pascal Bonnet · 2023 · International Journal of Molecular Sciences
  75. FlowDock : Geometric flow matching for generative protein–ligand docking and affinity prediction
    Alex Morehead, Jianlin Cheng · 2025 · Bioinformatics
  76. Quantifying evolutionary importance of protein sites: A Tale of two measures
    Avital Sharir‐Ivry, Yu Xia · 2021 · PLoS Genetics
  77. Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces
    Ruzianisra Mohamed, Jennifer Degac, Volkhard Helms · 2015 · PLoS ONE
  78. In silico structure‐activity‐relationship (SAR) models from machine learning: a review
    Xia Ning, George Karypis · 2010 · Drug Development Research
  79. Simplified AutoDock force field for hydrated binding sites
    Marek Wojciechowski · 2017 · Journal of Molecular Graphics and Modelling
  80. Scoring Functions for Protein–Ligand Interactions
    Sotriffer, Christoph · 2012 · Protein‐Ligand Interactions
  81. Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation
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  82. Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning
    Hui Zhu, Xuelian Li, Baoquan Chen, Niu Huang · 2025 · npj Drug Discovery.
  83. Prioritization of natural compounds against mycobacterium tuberculosis 3-dehydroquinate dehydratase: A combined in-silico and in-vitro study
    Mohsin Yousuf Lone, Mohd Athar, Vivek Kumar Gupta, Prakash Chandra Jha · 2017 · Biochemical and Biophysical Research Communications
  84. A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein–Protein Interaction Interfaces
    David Daqiang Xu, Yubing Si, Samy O. Meroueh · 2017 · Journal of Chemical Information and Modeling
  85. Computer‐Guided Trypanocidal Activity of Natural Lactones Produced by Endophytic Fungus of Euphorbia umbellata
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  86. From drugs to targets: Reverse engineering the virtual screening process on a proteomic scale
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  87. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery
    Leticia Manen-Freixa, Albert A. Antolín · 2024 · Expert Opinion on Drug Discovery
  88. Capturing Structure−Activity Relationships from Chemogenomic Spaces
    Bernd Wendt, Ulrike Uhrig, Fabian Bös · 2011 · Journal of Chemical Information and Modeling
  89. Exploration of Mycobacterium tuberculosis structural proteome: An in-silico approach
    Mohsin Yousuf Lone, Sivakumar Prasanth Kumar, Mohd Athar, Prakash Chandra Jha · 2017 · Journal of Theoretical Biology
  90. Protein–ligand interaction databases: advanced tools to mine activity data and interactions on a structural level
    Therese Inhester, Matthias Rarey · 2014 · Wiley Interdisciplinary Reviews Computational Molecular Science
  91. ProtChemSI: a network of protein-chemical structural interactions
    Kalinina, O. V., Wichmann, O., Apic, G., Russell, R. B. · 2012 · Nucleic Acids Research
  92. Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies
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  93. Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome
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  94. Non-catalytic Binding Sites Induce Weaker Long-Range Evolutionary Rate Gradients than Catalytic Sites in Enzymes
    Avital Sharir‐Ivry, Yu Xia · 2019 · Journal of Molecular Biology
  95. Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network
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  96. Targeting druggable enzymome by exploiting natural medicines: Anin silico–in vitrointegrated approach to combating multidrug resistance in bacterial infection
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  97. LIGYSIS-web: a resource for the analysis of protein-ligand binding sites
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  98. Unlocking Precision Docking for Metalloproteins
    Camila Mara Clemente, Juan M. Prieto, Marcelo Adrian Marti · 2024 · Journal of Chemical Information and Modeling
  99. CavBench: A benchmark for protein cavity detection methods
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  100. Generation of Pairwise Potentials Using Multidimensional Data Mining
    Zheng Zheng, Jun Pei, Nupur Bansal, Hao Liu, Lin Frank Song, Kenneth M. Merz · 2018 · Journal of Chemical Theory and Computation