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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach
Joseph A. Morrone, Jeffrey K. Weber, Tien Huynh, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 9, pp. 4170-4179
Open Access | Times Cited: 86

Showing 26-50 of 86 citing articles:

Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning
Liangxu Xie, Lei Xu, Ren Kong, et al.
Frontiers in Pharmacology (2020) Vol. 11
Open Access | Times Cited: 50
RNAPosers: Machine Learning Classifiers for Ribonucleic Acid–Ligand Poses
Sahil Chhabra, Jingru Xie, Aaron T. Frank
The Journal of Physical Chemistry B (2020) Vol. 124, Iss. 22, pp. 4436-4445
Closed Access | Times Cited: 44
GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer
Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, et al.
(2020), pp. 1-10
Open Access | Times Cited: 44
The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
Chao Shen, Xueping Hu, Junbo Gao, et al.
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 39
Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering
Juan J. Pérez, Román A. Pérez, Alberto Pérez
Frontiers in Molecular Biosciences (2021) Vol. 8
Open Access | Times Cited: 33
Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning
Tiejun Dong, Ziduo Yang, Jun Zhou, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 22, pp. 8446-8459
Closed Access | Times Cited: 16
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
Tobias Harren, Torben Gutermuth, Christoph Grebner, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 3
Closed Access | Times Cited: 6
MCN-CPI: Multiscale Convolutional Network for Compound–Protein Interaction Prediction
Shuang Wang, Mingjian Jiang, Shugang Zhang, et al.
Biomolecules (2021) Vol. 11, Iss. 8, pp. 1119-1119
Open Access | Times Cited: 31
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
QHwan Kim, Joon-Hyuk Ko, Sunghoon Kim, et al.
Bioinformatics (2021) Vol. 37, Iss. 20, pp. 3428-3435
Open Access | Times Cited: 30
SMMPPI: a machine learning-based approach for prediction of modulators of protein–protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2
Priya Gupta, Debasisa Mohanty
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Open Access | Times Cited: 29
Marine Alga Ulva fasciata-Derived Molecules for the Potential Treatment of SARS-CoV-2: An In Silico Approach
Haresh S. Kalasariya, Nikunj B. Patel, Amel Gacem, et al.
Marine Drugs (2022) Vol. 20, Iss. 9, pp. 586-586
Open Access | Times Cited: 21
Developing a Naïve Bayesian Classification Model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations
Yingmin Jiang, Wendian Xiong, Lei Jia, et al.
European Journal of Medicinal Chemistry (2022) Vol. 244, pp. 114824-114824
Closed Access | Times Cited: 19
Deep learning for predicting synergistic drug combinations: State‐of‐the‐arts and future directions
Yu Wang, Junjie Wang, Yun Liu
Clinical and Translational Discovery (2024) Vol. 4, Iss. 3
Open Access | Times Cited: 4
Accurate predictions of aqueous solubility of drug moleculesviathe multilevel graph convolutional network (MGCN) and SchNet architectures
Peng Gao, Jie Zhang, Yuzhu Sun, et al.
Physical Chemistry Chemical Physics (2020) Vol. 22, Iss. 41, pp. 23766-23772
Closed Access | Times Cited: 28
Beyond sequence: Structure-based machine learning
Janani Durairaj, Dick de Ridder, Aalt D. J. van Dijk
Computational and Structural Biotechnology Journal (2022) Vol. 21, pp. 630-643
Open Access | Times Cited: 17
Virtual screening: hope, hype, and the fine line in between
Hossam Nada, Nicholas A. Meanwell, Moustafa T. Gabr
Expert Opinion on Drug Discovery (2025)
Closed Access
Sensing Compound Substructures Combined with Molecular Fingerprinting to Predict Drug-Target Interactions
Huang Wan-hua, Xuecong Tian, Su Ying, et al.
Interdisciplinary Sciences Computational Life Sciences (2025)
Closed Access
Orion ® A Cloud‐Native Molecular Design Platform
Jesper Givskov Sørensen, Caitlin C. Bannan, Gaetano Calabrò, et al.
(2024), pp. 579-615
Closed Access | Times Cited: 3
Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction
Donghyuk Suh, Jai Woo Lee, Sun Choi, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 11, pp. 6032-6032
Open Access | Times Cited: 20
DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features
Agamemnon Krasoulis, Nick Antonopoulos, Vassilis Pitsikalis, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 19, pp. 4642-4659
Open Access | Times Cited: 14
DrugormerDTI: Drug Graphormer for drug–target interaction prediction
Jiayue Hu, Wang Yu, Chao Pang, et al.
Computers in Biology and Medicine (2023) Vol. 161, pp. 106946-106946
Closed Access | Times Cited: 8
A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection
José Jiménez-Luna, Alberto Cuzzolin, Giovanni Bolcato, et al.
Molecules (2020) Vol. 25, Iss. 11, pp. 2487-2487
Open Access | Times Cited: 20
Simplified, interpretable graph convolutional neural networks for small molecule activity prediction
Jeffrey K. Weber, Joseph A. Morrone, Sugato Bagchi, et al.
Journal of Computer-Aided Molecular Design (2021) Vol. 36, Iss. 5, pp. 391-404
Open Access | Times Cited: 18
ClassyPose: A Machine‐Learning Classification Model for Ligand Pose Selection Applied to Virtual Screening in Drug Discovery
Viet‐Khoa Tran‐Nguyen, Anne‐Claude Camproux, Olivier Taboureau
Advanced Intelligent Systems (2024) Vol. 6, Iss. 12
Open Access | Times Cited: 2
Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses
Fergus Boyles, Charlotte M. Deane, Garrett M. Morris
Journal of Chemical Information and Modeling (2021) Vol. 62, Iss. 22, pp. 5329-5341
Open Access | Times Cited: 15

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