Computational Methods in Drug Discovery and Development
Abstract
The rapid advancements in computational methods have revolutionized drug dis-covery and development. These methods, ranging from molecular modelling to ma-chine learning algorithms, have drastically increased in number and sophistication. However, a comprehensive understanding of these diverse approaches is essential for researchers aiming to make significant contributions to this evolving field. This review aims to provide a detailed overview of the most prominent computational methods currently used in drug discovery. It will analyze their underlying principles, discuss their applications, and highlight their potential for future advancements in the field. Through this examination, we aim to equip researchers with the necessary insights to navigate and contribute to the rapidly expanding landscape of computational drug discovery.
References
Abhishek and Neeru Jindal. Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation. Multimedia Tools and Applications, 80(3):3571–3599, 2021.
Laeeq Ahmed, Hiba Alogheli, Staffan Arvidsson McShane, Jonathan Alvarsson, Arvid Berg, Anders Larsson, Wesley Schaal, Erwin Laure, and Ola Spjuth. Predicting target profiles with confidence as a service using docking scores. Journal of Cheminformatics, 12:1–11, 2020.
Wafa Mohamed Al Madhagi. Importance and application of computational studies in finding new active quinazoline derivatives. In Recent Advances on Quinazoline. IntechOpen, 2023.
Hiba Alogheli, Gustav Olanders, Wesley Schaal, Peter Brandt, and Anders Karlen. ? Docking of macrocycles: comparing rigid and flexible docking in glide. Journal of chemical information and modeling, 57(2):190–202, 2017.
Rommie E Amaro. Will the real cryptic pocket please stand out? Biophysical Journal, 116(5):753–754, 2019
Dinler A Antunes, Didier Devaurs, and Lydia E Kavraki. Understanding the challenges of protein flexibility in drug design. Expert opinion on drug discovery, 10(12):1301–1313, 2015
Olayide A Arodola and Mahmoud ES Soliman. Quantum mechanics implementation in drug-design workflows: does it really help? Drug design, development and therapy, pages 2551–2564, 2017
Michael Ashburner, Catherine A Ball, Judith A Blake, David Botstein, Heather Butler, J Michael Cherry, Allan P Davis, Kara Dolinski, Selina S Dwight, Janan T Eppig, et al. Gene ontology: tool for the unification of biology. Nature genetics, 25(1):25–29, 2000. 36
Kenneth Atz, Francesca Grisoni, and Gisbert Schneider. Geometric deep learning on molecular representations. Nature Machine Intelligence, 3(12):1023–1032, 2021
Wail Ba-Alawi, Othman Soufan, Magbubah Essack, Panos Kalnis, and Vladimir B Bajic. Daspfind: new efficient method to predict drug–target interactions. Journal of cheminformatics, 8:1–9, 2016
Libero J Bartolotti and Ken Flurchick. An introduction to density functional theory. Reviews in computational chemistry, pages 187–216, 1996
Andreas Bender, Josef Scheiber, Meir Glick, John W Davies, Kamal Azzaoui, Jacques Hamon,
Laszlo Urban, Steven Whitebread, and Jeremy L Jenkins. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem: Chemistry Enabling Drug Discovery, 2(6):861–873, 2007
Feliks Aleksandrovich Berezin and Mikhail Shubin. The Schrodinger Equation ? , volume 66.
Springer Science & Business Media, 2012
Oliver Buß, Jens Rudat, and Katrin Ochsenreither. Foldx as protein engineering tool: better than random based approaches? Computational and structural biotechnology journal, 16:25–33, 2018
Alexander Button, Daniel Merk, Jan A Hiss, and Gisbert Schneider. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nature machine intelligence, 1(7):307–315, 2019
Dong-Sheng Cao, Zhen-Ke Deng, Min-Feng Zhu, Zhi-Jiang Yao, Jie Dong, and Rui-Gang Zhao, Ensemble partial least squares regression for descriptor selection, outlier detection, applicability domain assessment, and ensemble modeling in qsar/qspr modeling. Journal of Chemometrics, 31(11):e2922, 2017
J-M Cardot, A Garcia Arieta, P Paixao, I Tasevska, and B Davit. Implementing the
biopharmaceutics classification system in drug development: reconciling similarities, differences, and shared challenges in the ema and us-fda-recommended approaches. The AAPS journal, 18:1039–1046, 2016
Paula Carracedo-Reboredo, Jose Linares-Blanco, Nereida Rodr ? ??guez-Fernandez, ? Francisco Cedron, Francisco J Novoa, Adrian Carballal, Victor Maojo, Alejandro ? Pazos, and Carlos Fernandez-Lozano. A review on machine learning approaches and trends in drug discovery. Computational and structural biotechnology journal, 19:4538–4558, 2021
Claudio N Cavasotto, Natalia S Adler, and Maria G Aucar. Quantum chemical approaches in
structure-based virtual screening and lead optimization. Frontiers in chemistry, 6:188, 2018.
Jean-Pierre Changeux. The concept of allosteric modulation: an overview. Drug Discovery Today: Technologies, 10(2):e223–e228, 2013
Paul S Charifson, Joseph J Corkery, Mark A Murcko, and W Patrick Walters. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. Journal of medicinal chemistry, 42(25):5100–5109, 1999
Alexios Chatzigoulas and Zoe Cournia. Rational design of allosteric modulators: Challenges and successes. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(6):e1529, 2021
Fangling Chen, Zhuoya Wang, Chaoyi Wang, Qingliang Xu, Jiazhen Liang, Ximing Xu, Jinbo
Yang, Changyun Wang, Tao Jiang, and Rilei Yu. Application of reverse docking for target prediction of marine compounds with anti-tumor activity. Journal of Molecular Graphics and Modelling, 77:372–377, 2017
Hongming Chen, Thierry Kogej, and Ola Engkvist. Cheminformatics in drug discovery, an
industrial perspective. Molecular Informatics, 37(9-10):1800041, 2018
Rong Chen, Li Li, and Zhiping Weng. Zdock: an initial-stage protein-docking algorithm. Proteins: Structure, Function, and Bioinformatics, 52(1):80–87, 2003
Ruolan Chen, Xiangrong Liu, Shuting Jin, Jiawei Lin, and Juan Liu. Machine learning for drug-target interaction prediction. Molecules, 23(9):2208, 2018
Yu-Chian Chen. Beware of docking! Trends in pharmacological sciences, 36(2):78–95, 2015
Tammy Man-Kuang Cheng, Tom L Blundell, and Juan Fernandez-Recio. pydock: Electrostatics and desolvation for effective scoring of rigid-body protein–protein docking. Proteins: Structure, Function, and Bioinformatics, 68(2):503–515, 2007
Gaurav Chopra and Ram Samudrala. Exploring polypharmacology in drug discovery and
repurposing using the cando platform. Current pharmaceutical design, 22(21):3109–3123, 2016
Arthur Christopoulos. Allosteric binding sites on cell-surface receptors: novel targets for drug discovery. Nature reviews Drug discovery, 1(3):198–210, 2002
Maciej Pawel Ciemny, Mateusz Kurcinski, Andrzej Kolinski, and Sebastian Kmiecik. Towards
protein-protein docking with significant structural changes using cabs-dock. arXiv preprint
arXiv:1605.09266, 2016
Peter Cimermancic, Patrick Weinkam, T Justin Rettenmaier, Leon Bichmann, Daniel A Keedy, Rahel A Woldeyes, Dina Schneidman-Duhovny, Omar N Demerdash, Julie C Mitchell, James A Wells, et al. Cryptosite: expanding the druggable proteome by characterization and prediction of cryptic binding sites. Journal of molecular biology, 428(4):709–719, 2016
Natanya Civjan. Chemical biology: approaches to drug discovery and development to targeting disease. John Wiley & Sons, 2012
Robert A Copeland. Evaluation of enzyme inhibitors in drug discovery: a guide for medicinal chemists and pharmacologists. John Wiley & Sons, 2013
Jason B Cross, David C Thompson, Brajesh K Rai, J Christian Baber, Kristi Yi Fan, Yongbo Hu, and Christine Humblet. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. Journal of chemical information and modeling, 49(6):1455–1474, 2009
Peter Csermely, Robin Palotai, and Ruth Nussinov. Induced fit, conformational selection and independent dynamic segments: an extended view of binding events. Trends in biochemical sciences, 35(10):539–546, 2010
Sheisi FL da Silva Rocha, Carolina G Olanda, Harold H Fokoue, and Carlos MR Sant’Anna.
Virtual screening techniques in drug discovery: review and recent applications. Current topics in medicinal chemistry, 19(19):1751–1767, 2019
Pankaj R Daga, Ronak Y Patel, and Robert J Doerksen. Template-based protein modeling: recent methodological advances. Current topics in medicinal chemistry, 10(1):84–94, 2010
Andrew M Davis, Simon J Teague, and Gerard J Kleywegt. Application and limitations of x-ray crystallographic data in structure-based ligand and drug design. Angewandte Chemie International Edition, 42(24):2718–2736, 2003
Sjoerd J de Vries, Juli weben Rey, Christina EM Schindler, Martin Zacharias, and Pierre Tuffery. The pepattract server for blind, large-scale peptide–protein docking. Nucleic Acids Research, 45(W1):W361–W364, 2017
Sjoerd J de Vries, Christina EM Schindler, Isaure Chauvot de Beauchene, and ˆ Martin Zacharias. A web interface for easy flexible protein-protein docking with attract. Biophysical journal, 108(3):462–465, 2015
Gregory J Digby, P Jeffrey Conn, and Craig W Lindsley. Orthosteric-and allostericinduced ligand-directed trafficking at gpcrs. Current opinion in drug discovery & development, 13(5):587, 2010
David J Diller and Christophe LMJ Verlinde. A critical evaluation of several global optimization algorithms for the purpose of molecular docking. Journal of computational chemistry, 20(16):1740–1751, 1999
Joseph A DiMasi, Henry G Grabowski, and Ronald W Hansen. Innovation in the pharmaceutical industry: new estimates of r&d costs. Journal of health economics, 47:20–33, 2016
Stefan Doerr, Maciej Majewski, Adria P` erez, Andreas Kramer, Cecilia Clementi, ´ Frank Noe, Toni Giorgino, and Gianni De Fabritiis. Torchmd: A deep learning framework for molecular simulations. Journal of chemical theory and computation, 17(4):2355–2363, 2021
Ryan JO Dowling, Ivan Topisirovic, Bruno D Fonseca, and Nahum Sonenberg. Dissecting the role of mtor: lessons from mtor inhibitors. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1804(3):433–439, 2010
Oranit Dror, Alexandra Shulman-Peleg, Ruth Nussinov, and Haim J Wolfson. Predicting molecular interactions in silico: I. a guide to pharmacophore identification and its applications to drug design. Current medicinal chemistry, 11(1):71–90, 2004
Dina Duhovny, Ruth Nussinov, and Haim J Wolfson. Efficient unbound docking of rigid molecules. In Algorithms in Bioinformatics: Second International Workshop, WABI 2002 Rome, Italy, September 17–21, 2002 Proceedings 2, pages 185–200. Springer, 2002
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli. Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings. Journal of chemical information and modeling, 61(8):3891–3898, 2021
Christiane Ehrt, Tobias Brinkjost, and Oliver Koch. Impact of binding site comparisons on medicinal chemistry and rational molecular design. Journal of medicinal chemistry, 59(9):4121–4151, 2016
David Eisenberg, Edward M Marcotte, Ioannis Xenarios, and Todd O Yeates. Protein function in the post-genomic era. Nature, 405(6788):823–826, 2000
Murtala A Ejalonibu, Ahmed A Elrashedy, Monsurat M Lawal, Mahmoud E Soliman, Sphelele C Sosibo, Hezekiel M Kumalo, and Ndumiso N Mhlongo. Dual targeting approach for mycobacterium tuberculosis drug discovery: Insights from dft calculations and molecular dynamics simulations. Structural Chemistry, 31:557– 571, 2020
Murtala A Ejalonibu, Segun A Ogundare, Ahmed A Elrashedy, Morufat A Ejalonibu, Monsurat M Lawal, Ndumiso N Mhlongo, and Hezekiel M Kumalo. Drug discovery for mycobacterium tuberculosis using structure-based computer-aided drug design approach. International Journal of Molecular Sciences, 22(24):13259, 2021
Todd JA Ewing, Shingo Makino, A Geoffrey Skillman, and Irwin D Kuntz. Dock 4.0: search strategies for automated molecular docking of flexible molecule databases. Journal of computer-aided molecular design, 15:411–428, 2001
Thomas Eckart Exner, Oliver Korb, and Tim Ten Brink. New and improved features of the docking software plants. Chemistry Central Journal, 3(1):1–1, 2009
Federico Falchi, Fabiana Caporuscio, and Maurizio Recanatini. Structure-based design of small-molecule protein–protein interaction modulators: the story so far. Future medicinal chemistry, 6(3):343–357, 2014
Qingyuan Feng, Evgenia Dueva, Artem Cherkasov, and Martin Ester. Padme: A deep learning-based framework for drug-target interaction prediction. arXiv preprint arXiv:1807.09741, 2018
Philippe Ferrara, Holger Gohlke, Daniel J Price, Gerhard Klebe, and Charles L Brooks. Assessing scoring functions for protein- ligand interactions. Journal of medicinal chemistry, 47(12):3032–3047, 2004
Jonathan Fine, Janez Konc, Ram Samudrala, and Gaurav Chopra. Candock: Chemical atomic network-based hierarchical flexible docking algorithm using generalized statistical potentials. Journal of chemical information and modeling, 60(3):1509–1527, 2020
Thomas Force and Kyle L Kolaja. Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes. Nature reviews Drug discovery, 10(2):111–126, 2011
Cen Gao, Jeremy Desaphy, and Michal Vieth. Are induced fit protein conformational changes caused by ligand-binding predictable? a molecular dynamics investigation. Journal of computational chemistry, 38(15):1229–1237, 2017
Rafael Gomez-Bombarelli, Jennifer N Wei, David Duvenaud, Jos ´ e Miguel ´ Hernandez-Lobato, Benjam ´ ´?n Sanchez-Lengeling, Dennis Sheberla, Jorge ´ Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alan Aspuru- ´ Guzik. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2):268–276, 2018
Nina M Goodey and Stephen J Benkovic. Allosteric regulation and catalysis emerge via a common route. Nature chemical biology, 4(8):474–482, 2008
Palash Goyal and Emilio Ferrara. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems, 151:78–94, 2018
Marianne A Grant. Protein structure prediction in structure-based ligand design and virtual screening. Combinatorial chemistry & high throughput screening, 12(10):940–960, 2009
Bartosz A Grzybowski, Alexey V Ishchenko, Jun Shimada, and Eugene I Shakhnovich. From knowledge-based potentials to combinatorial lead design in silico. Accounts of chemical research, 35(5):261–269, 2002
Isabella A Guedes, Felipe SS Pereira, and Laurent E Dardenne. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Frontiers in pharmacology, 9:1089, 2018
Alexis S Hammond, Alice L Rodriguez, Steven D Townsend, Colleen M Niswender, Karen J Gregory, Craig W Lindsley, and P Jeffrey Conn. Discovery of a novel chemical class of mglu5 allosteric ligands with distinct modes of pharmacology. ACS chemical neuroscience, 1(10):702–716, 2010
Markus Hartenfeller and Gisbert Schneider. De novo drug design. Chemoinformatics and computational chemical biology, pages 299–323, 2011. 41
Stefan Henrich, Outi MH Salo-Ahen, Bingding Huang, Friedrich F Rippmann, Gabriele Cruciani, and Rebecca C Wade. Computational approaches to identifying and characterizing protein binding sites for ligand design. Journal of Molecular Recognition: An Interdisciplinary Journal, 23(2):209–219, 2010
Andrew L Hopkins. Network pharmacology: the next paradigm in drug discovery. Nature chemical biology, 4(11):682–690, 2008
Kun-Yi Hsin, Samik Ghosh, and Hiroaki Kitano. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PloS one, 8(12):e83922, 2013
Sheng-You Huang, Min Li, Jianxin Wang, and Yi Pan. Hybriddock: a hybrid protein–ligand docking protocol integrating protein-and ligand-based approaches. Journal of Chemical Information and Modeling, 56(6):1078–1087, 2016
Georgios Iakovou. Simulating molecular docking with haptics. PhD thesis, University of East Anglia, Norwich, UK, 2015
Alexey V Ishchenko and Eugene I Shakhnovich. Small molecule growth 2001 (smog2001): An improved knowledge-based scoring function for protein-ligand interactions. Journal of medicinal chemistry, 45(13):2770–2780, 2002
Md Ashraful Islam. Atomlbs: An atom based convolutional neural network for druggable ligand binding site prediction. Master’s thesis, The University of Texas Rio Grande Valley, 2022
Reed B Jacob, Tim Andersen, and Owen M McDougal. Accessible highthroughput virtual screening molecular docking software for students and educators. PLoS computational biology, 8(5):e1002499, 2012
Ursula Jakob, Richard Kriwacki, and Vladimir N Uversky. Conditionally and transiently disordered proteins: awakening cryptic disorder to regulate protein function. Chemical reviews, 114(13):6779–6805, 2014
Mohammad Hasan Jamei, Mehdi Khoshneviszadeh, Najmeh Edraki, Maryam Firoozi, Zahra Haghighijoo, Rmin Miri, and Amirhossein Sakhtaman. Cross docking study directed toward virtual screening and molecular docking study of phenanthrene 1, 2, 4-triazine derivatives as novel bcl-2 inhibitors. Trends in Pharmaceutical Sciences, 2(4):253–258, 2016
C John Harris, Richard D Hill, David W Sheppard, Martin J Slater, and Pieter FW Stouten. The design and application of target-focused compound libraries. Combinatorial chemistry & high throughput screening, 14(6):521–531, 2011
Minoru Kanehisa. The kegg database. In ‘In silico’simulation of biological processes: Novartis Foundation Symposium 247, volume 247, pages 91–103. Wiley Online Library, 2002
Gozde Kar, Ozlem Keskin, Attila Gursoy, and Ruth Nussinov. Allostery and population shift in drug discovery. Current opinion in pharmacology, 10(6):715–722, 2010
Supratik Kar and Jerzy Leszczynski. Recent advances of computational modeling for predicting drug metabolism: a perspective. Current Drug Metabolism, 18(12):1106–1122, 2017
Kristian W Kaufmann and Jens Meiler. Using rosetta ligand for small molecule docking into comparative models. PloS one, 7(12):e50769, 2012
Aman Chandra Kaushik, Aamir Mehmood, Dong-Qing Wei, Sadia Nawab, Shakti Sahi, and Ajay Kumar. Cheminformatics and bioinformatics at the interface with systems biology: bridging chemistry and medicine, volume 24. Royal Society of Chemistry, 2023
Terry Kenakin and Arthur Christopoulos. Analytical pharmacology: the impact of numbers on pharmacology. Trends in pharmacological sciences, 32(4):189–196, 2011
Prashant S Kharkar, Sona Warrier, and Ram S Gaud. Reverse docking: a powerful tool for drug repositioning and drug rescue. Future medicinal chemistry, 6(3):333– 342, 2014
Samima Khatun, Rinki Bhagat, Sk Abdul Amin, Tarun Jha, and Shovanlal Gayen. Density functional theory (dft) studies in hdac-based chemotherapeutics: Current findings, case studies and future perspectives. Computers in Biology and Medicine, page 108468, 2024
Deok-Soo Kim, Chong-Min Kim, Chung-In Won, Jae-Kwan Kim, Joonghyun Ryu, Youngsong Cho, Changhee Lee, and Jong Bhak. Betadock: shape-priority docking method based on beta-complex. Journal of Biomolecular Structure and Dynamics, 29(1):219–242, 2011
RyangGuk Kim, Rosario I Corona, Bo Hong, and Jun-tao Guo. Benchmarks for flexible and rigid transcription factor-dna docking. BMC structural biology, 11:1– 10, 2011
Oliver Korb, Thomas Stutzle, and Thomas E Exner. Empirical scoring functions for advanced protein- ligand docking with plants. Journal of chemical information and modeling, 49(1):84–96, 2009
Bernd Kramer, Matthias Rarey, and Thomas Lengauer. Evaluation of the flexx incremental construction algorithm for protein–ligand docking. Proteins: Structure, Function, and Bioinformatics, 37(2):228–241, 1999
Jacek Kujawski, Hanna Popielarska, Anna Myka, Beata Drabinska, and Marek K ´ Bernard. The log p parameter as a molecular descriptor in the computer-aided drug design–an overview. Computational Methods in Science and Technology, 18(2):81–88, 2012
Mateusz Kurcinski, Michal Jamroz, Maciej Blaszczyk, Andrzej Kolinski, and Sebastian Kmiecik. Cabs-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic acids research, 43(W1):W419–W424, 2015
Antonija Kuzmanic, Gregory R Bowman, Jordi Juarez-Jimenez, Julien Michel, and Francesco L Gervasio. Investigating cryptic binding sites by molecular dynamics simulations. Accounts of chemical research, 53(3):654–661, 2020
Margherita Lapillo, Tiziano Tuccinardi, Adriano Martinelli, Marco Macchia, Antonio Giordano, and Giulio Poli. Extensive reliability evaluation of docking-based target-fishing strategies. International journal of molecular sciences, 20(5):1023, 2019
Vy TT Le, Tu HT Nguyen, and Phuc-Chau Do. Global ligand-protein docking tools: Comparation and case study. 2024
Vincent Le Guilloux, Peter Schmidtke, and Pierre Tuffery. Fpocket: an open source platform for ligand pocket detection. BMC bioinformatics, 10:1–11, 2009
Dong-Dong Li, Xiang-Feng Meng, Qiang Wang, Pan Yu, Lin-Guo Zhao, ZhengPing Zhang, Zhen-Zhong Wang, and Wei Xiao. Consensus scoring model for the molecular docking study of mtor kinase inhibitor. Journal of Molecular Graphics and Modelling, 79:81–87, 2018
Jin Li, Ailing Fu, and Le Zhang. An overview of scoring functions used for protein–ligand interactions in molecular docking. Interdisciplinary Sciences: Computational Life Sciences, 11:320–328, 2019
Li Li, Rong Chen, and Zhiping Weng. Rdock: refinement of rigid-body protein docking predictions. Proteins: Structure, Function, and Bioinformatics, 53(3):693–707, 2003
Xiaobai Li, Yingyi Chen, Shaoyong Lu, Zhimin Huang, Xinyi Liu, Qi Wang, Ting Shi, and Jian Zhang. Toward an understanding of the sequence and structural basis of allosteric proteins. Journal of Molecular Graphics and Modelling, 40:30–39, 2013
Yibo Li, Liangren Zhang, and Zhenming Liu. Multi-objective de novo drug design with conditional graph generative model. Journal of cheminformatics, 10:1–24, 2018
Christopher A Lipinski, Franco Lombardo, Beryl W Dominy, and Paul J Feeney. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 23(1-3):3–25, 1997
Jie Liu and Renxiao Wang. Classification of current scoring functions. Journal of chemical information and modeling, 55(3):475–482, 2015
Kai Liu and Hironori Kokubo. Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations: a cross-docking study. Journal of chemical information and modeling, 57(10):2514–2522, 2017
Xuewei Liu, Danfeng Shi, Shuangyan Zhou, Hongli Liu, Huanxiang Liu, and Xiaojun Yao. Molecular dynamics simulations and novel drug discovery. Expert opinion on drug discovery, 13(1):23–37, 2018
Yang Liu, Maximilian Grimm, Wen-tao Dai, Mu-chun Hou, Zhi-Xiong Xiao, and Yang Cao. Cb-dock: A web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacologica Sinica, 41(1):138–144, 2020
Yang Liu, Xiaocong Yang, Jianhong Gan, Shuang Chen, Zhi-Xiong Xiao, and Yang Cao. Cb-dock2: Improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Research, 50(W1):W159–W164, 2022
Yu-Chen Lo, Stefano E Rensi, Wen Torng, and Russ B Altman. Machine learning in chemoinformatics and drug discovery. Drug discovery today, 23(8):1538–1546, 2018
Nir London, Barak Raveh, Eyal Cohen, Guy Fathi, and Ora Schueler-Furman. Rosetta flexpepdock web server—high resolution modeling of peptide–protein interactions. Nucleic acids research, 39(suppl 2):W249–W253, 2011
Shaoyong Lu, Wenkang Huang, and Jian Zhang. Recent computational advances in the identification of allosteric sites in proteins. Drug discovery today, 19(10):1595– 1600, 2014
Shaoyong Lu, Shuai Li, and Jian Zhang. Harnessing allostery: a novel approach to drug discovery. Medicinal research reviews, 34(6):1242–1285, 2014
Ying Lu, Sungwon Kim, and Kinam Park. In vitro–in vivo correlation: Perspectives on model development. International journal of pharmaceutics, 418(1):142–148, 2011
R Frederick Ludlow, Marcel L Verdonk, Harpreet K Saini, Ian J Tickle, and Harren Jhoti. Detection of secondary binding sites in proteins using fragment screening. Proceedings of the National Academy of Sciences, 112(52):15910–15915, 2015
Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen, and Jianyang Zeng. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature communications, 8(1):573, 2017
Buyong Ma, Tal Elkayam, Haim Wolfson, and Ruth Nussinov. Protein–protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proceedings of the National Academy of Sciences, 100(10):5772–5777, 2003
Xiaomin Ma, Hu Meng, and Luhua Lai. Motions of allosteric and orthosteric ligand-binding sites in proteins are highly correlated. Journal of Chemical Information and Modeling, 56(9):1725–1733, 2016
Rucha Mahadik, Paul Kiptoo, Tom Tolbert, and Teruna J Siahaan. Immune modulation by antigenic peptides and antigenic peptide conjugates for treatment of multiple sclerosis. Medical research archives, 10(5), 2022
Shingo Makino, Todd JA Ewing, and Irwin D Kuntz. Dream++: flexible docking program for virtual combinatorial libraries. Journal of computer-aided molecular design, 13:513–532, 1999.
Ryan J Malonis, Jonathan R Lai, and Olivia Vergnolle. Peptide-based vaccines: current progress and future challenges. Chemical reviews, 120(6):3210–3229, 2019.
Dominic D Martinelli. Generative machine learning for de novo drug discovery: A systematic review. Computers in Biology and Medicine, 145:105403, 2022.
Karina Martinez-Mayorga, Abraham Madariaga-Mazon, Jose L Medina-Franco, ´ and Gerald Maggiora. The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert opinion on drug discovery, 15(3):293–306, 2020.
Gerard Martinez-Rosell, Toni Giorgino, Matt J Harvey, and Gianni de Fabritiis. Drug discovery and molecular dynamics: methods, applications and perspective beyond the second timescale. Current topics in medicinal chemistry, 17(23):2617– 2625, 2017.
Xuan-Yu Meng, Hong-Xing Zhang, Mihaly Mezei, and Meng Cui. Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design, 7(2):146–157, 2011.
Madhuchhanda Mohanty and Priti S Mohanty. Molecular docking in organic, inorganic, and hybrid systems: a tutorial review. Monatshefte fur Chemie-Chemical ¨ Monthly, 154(7):683–707, 2023.
Klaus Mohr, Christian Trankle, Evi Kostenis, Elisabetta Barocelli, Marco De Am- ¨ ici, and Ulrike Holzgrabe. Rational design of dualsteric gpcr ligands: quests and promise. British journal of pharmacology, 159(5):997–1008, 2010.
Tobias Morawietz and Nongnuch Artrith. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications. Journal of Computer-Aided Molecular Design, 35(4):557–586, 2021.
Hesam N Motlagh, James O Wrabl, Jing Li, and Vincent J Hilser. The ensemble nature of allostery. Nature, 508(7496):331–339, 2014.
Varnavas D Mouchlis, Antreas Afantitis, Angela Serra, Michele Fratello, Anastasios G Papadiamantis, Vassilis Aidinis, Iseult Lynch, Dario Greco, and Georgia Melagraki. Advances in de novo drug design: from conventional to machine learning methods. International journal of molecular sciences, 22(4):1676, 2021.
Christa E Muller, Anke C Schiedel, and Younis Baqi. Allosteric modulators of ¨ rhodopsin-like g protein-coupled receptors: opportunities in drug development. Pharmacology & therapeutics, 135(3):292–315, 2012.
Ruth Nussinov and Chung-Jung Tsai. The different ways through which specificity works in orthosteric and allosteric drugs. Current pharmaceutical design, 18(9):1311–1316, 2012.
Ruth Nussinov and Chung-Jung Tsai. Allostery in disease and in drug discovery. Cell, 153(2):293–305, 2013.
Ruth Nussinov and Chung-Jung Tsai. The design of covalent allosteric drugs. Annual review of pharmacology and toxicology, 55(1):249–267, 2015.
Marc Nathan Offman. Protein structure prediction and refinement. University of London, University College London (United Kingdom), 2008.
Masahito Ohue, Takehiro Shimoda, Shuji Suzuki, Yuri Matsuzaki, Takashi Ishida, and Yutaka Akiyama. Megadock 4.0: an ultra–high-performance protein– protein docking software for heterogeneous supercomputers. Bioinformatics, 30(22):3281–3283, 2014.
Vladimiras Oleinikovas, Giorgio Saladino, Benjamin P Cossins, and Francesco L Gervasio. Understanding cryptic pocket formation in protein targets by enhanced sampling simulations. Journal of the American Chemical Society, 138(43):14257– 14263, 2016.
Hakime Ozt ¨ urk, Elif Ozkirimli, and Arzucan ¨ Ozg ¨ ur. A comparative study of ¨ smiles-based compound similarity functions for drug-target interaction prediction. BMC bioinformatics, 17:1–11, 2016.
Nataraj S Pagadala, Khajamohiddin Syed, and Jack Tuszynski. Software for molecular docking: a review. Biophysical reviews, 9:91–102, 2017.
Musun Park, Sa-Yoon Park, Hae-Jeung Lee, and Chang-Eop Kim. A systems-level analysis of mechanisms of platycodon grandiflorum based on a network pharmacological approach. Molecules, 23(11):2841, 2018.
Alessio Peracchi and Andrea Mozzarelli. Exploring and exploiting allostery: Models, evolution, and drug targeting. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1814(8):922–933, 2011.
Yunierkis Perez-Castillo, Stellamaris Sotomayor-Burneo, Karina JimenesVargas, Mario Gonzalez-Rodriguez, Maykel Cruz-Monteagudo, Vinicio ArmijosJaramillo, M Natalia DS Cordeiro, Fernanda Borges, Aminael S ´ anchez-Rodr ´ ´?guez, and Eduardo Tejera. Compscore: boosting structure-based virtual screening performance by incorporating docking scoring function components into consensus scoring. Journal of chemical information and modeling, 59(9):3655–3666, 2019. 47
Kosmas Alexandros Pervanidis, Giovanni Danilo D’Angelo, Jorn Weisner, Sven ¨ Brandherm, and Daniel Rauh. Akt inhibitor advancements: From capivasertib approval to covalent-allosteric promises. Journal of Medicinal Chemistry, 67(8):6052–6063, 2024.
Brian G Pierce, Kevin Wiehe, Howook Hwang, Bong-Hyun Kim, Thom Vreven, and Zhiping Weng. Zdock server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics, 30(12):1771–1773, 2014.
Benoit Playe and Veronique Stoven. Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity. Journal of cheminformatics, 12(1):11, 2020.
Kathryn A Porter, Israel Desta, Dima Kozakov, and Sandor Vajda. What method to use for protein–protein docking? Current opinion in structural biology, 55:1–7, 2019.
Rajani Pydipalli. Network-based approaches in bioinformatics and cheminformatics: Leveraging it for insights. ABC Journal of Advanced Research, 7(2):139–150, 2018.
Hojjat Rakhshani, Lhassane Idoumghar, Julien Lepagnot, Mathieu Brevilliers, and ´ Edward Keedwell. Automatic hyperparameter selection in autodock. In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM), pages 734– 738. IEEE, 2018.
Olof Ramstrom and Jean-Marie Lehn. Drug discovery by dynamic combinatorial ¨ libraries. Nature Reviews Drug Discovery, 1(1):26–36, 2002.
L Ramya and N Gautham. Conformational space exploration of met-and leuenkephalin using the mols method, molecular dynamics, and monte carlo simulation—a comparative study. Biopolymers, 97(3):165–176, 2012.
Arjun Rao, Tin M Tunjic, Michael Brunsteiner, Michael Muller, Hosein Fooladi, ¨ Chiara Gasbarri, and Noah Weber. Bayesian optimization for ternary complex prediction (botcp). Artificial Intelligence in the Life Sciences, 3:100072, 2023.
Matthias Rarey, Bernd Kramer, Thomas Lengauer, and Gerhard Klebe. A fast flexible docking method using an incremental construction algorithm. Journal of molecular biology, 261(3):470–489, 1996.
Farshid Rayhan, Sajid Ahmed, Zaynab Mousavian, Dewan Md Farid, and Swakkhar Shatabda. Frnet-dti: Deep convolutional neural network for drug-target interaction prediction. Heliyon, 6(3), 2020.
Daniel Reker, Petra Schneider, Gisbert Schneider, and JB Brown. Active learning for computational chemogenomics. Future medicinal chemistry, 9(4):381–402, 2017.
Raquel Rodr´?guez-Perez, Filip Miljkovi ´ c, and J ´ urgen Bajorath. Machine learning ¨ in chemoinformatics and medicinal chemistry. Annual review of biomedical data science, 5(1):43–65, 2022.
Judith M Rollinger, Hermann Stuppner, and Thierry Langer. Virtual screening for the discovery of bioactive natural products. Natural compounds as drugs Volume I, pages 211–249, 2008.
J Rondeau, Gerhard Klebe, and Alberto Podjarny. Ligand binding: the crystallographic approach. Biophysical approaches determining ligand binding to biomolecular targets: detection, measurement and modelling. 1:56– 135, 2011.
R Rosenfeld, S Vajda, and C DeLisi. Flexible docking and design. Annual review of biophysics and biomolecular structure, 24(1):677–700, 1995.
Christopher D Rosin, R Scott Halliday, William E Hart, and Richard K Belew. A comparison of global and local search methods in drug docking. In ICGA, pages 221–229. Citeseer, 1997.
Ashish Runthala and Shibasish Chowdhury. Refined template selection and combination algorithm significantly improves template-based modeling accuracy. Journal of Bioinformatics and Computational Biology, 17(02):1950006, 2019.
Kanica Sachdev and Manoj K Gupta. A comprehensive review of computational techniques for the prediction of drug side effects. Drug Development Research, 81(6):650–670, 2020.
Adrien Saladin, Julien Rey, Pierre Thevenet, Martin Zacharias, Gautier Moroy, and ´ Pierre Tuffery. Pep-sitefinder: a tool for the blind identification of peptide binding ´ sites on protein surfaces. Nucleic acids research, 42(W1):W221–W226, 2014.
Outi MH Salo-Ahen, Ida Alanko, Rajendra Bhadane, Alexandre MJJ Bonvin, Rodrigo Vargas Honorato, Shakhawath Hossain, Andre H Juffer, Aleksei Kabedev, ´ Maija Lahtela-Kakkonen, Anders Støttrup Larsen, et al. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes, 9(1):71, 2020.
Samarth Sandeep, Vaibhav Gupta, and Torin Keenan. Utilizing quantum biological techniques on a quantum processing unit for improved protein binding site determination. BioRxiv, pages 2020–03, 2020.
Karina B Santos, Isabella A Guedes, Ana LM Karl, and Laurent E Dardenne. Highly flexible ligand docking: Benchmarking of the dockthor program on the leads-pep protein–peptide data set. Journal of Chemical Information and Modeling, 60(2):667–683, 2020.
Diogo Santos-Martins, Stefano Forli, Maria Joao Ramos, and Arthur J Olson. ˜ Autodock4zn: an improved autodock force field for small-molecule docking to zinc metalloproteins. Journal of chemical information and modeling, 54(8):2371– 2379, 2014.
Nicolas Sauton, David Lagorce, Bruno O Villoutreix, and Maria A Miteva. Msdock: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening. BMC bioinformatics, 9:1–12, 2008.
Petra Schneider and Gisbert Schneider. De novo design at the edge of chaos: Miniperspective. Journal of medicinal chemistry, 59(9):4077–4086, 2016.
Marwin HS Segler, Mike Preuss, and Mark P Waller. Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698):604–610, 2018.
Lucia Sessa, Luigi Di BIasi, Rosaura Parisi, Simona Concilio, and Stefano Piotto. Receptor flexibility in molecular cross-docking. PeerJ Preprints, 4:e2199v1, 2016.
Attila A Seyhan. Lost in translation: the valley of death across preclinical and clinical divide–identification of problems and overcoming obstacles. Translational Medicine Communications, 4(1):1–19, 2019.
Bilal Shaker, Myung-Sang Yu, Jingyu Lee, Yongmin Lee, Chanjin Jung, and Dokyun Na. User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation. Journal of Microbiology, 58:235–244, 2020.
Jamal Shamsara. Crossdocker: a tool for performing cross-docking using autodock vina. SpringerPlus, 5:1–5, 2016.
Takehiro Shimoda, Takashi Ishida, Shuji Suzuki, Masahito Ohue, and Yutaka Akiyama. Megadock-gpu: acceleration of protein-protein docking calculation on gpus. In Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, pages 883–889, 2013.
Woong-Hee Shin, Lim Heo, Juyong Lee, Junsu Ko, Chaok Seok, and Jooyoung Lee. Ligdockcsa: protein–ligand docking using conformational space annealing. Journal of computational chemistry, 32(15):3226–3232, 2011.
Peter K Sorger, Sandra RB Allerheiligen, Darrell R Abernethy, Russ B Altman, Kim LR Brouwer, Andrea Califano, David Z D’Argenio, Ravi Iyengar, William J Jusko, Richard Lalonde, et al. Quantitative and systems pharmacology in the postgenomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. In An NIH white paper by the QSP workshop group, volume 48, pages 1–47. NIH Bethesda Bethesda, 2011.
Cristoph Sotriffer and H Matter. Virtual screening. Wiley Online Library, 2011.
Francesca Stanzione, Ilenia Giangreco, and Jason C Cole. Use of molecular docking computational tools in drug discovery. Progress in medicinal chemistry, 60:273–343, 2021.
Maciej Staszak, Katarzyna Staszak, Karolina Wieszczycka, Anna Bajek, Krzysztof Roszkowski, and Bartosz Tylkowski. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. 50 Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(2):e1568, 2022.
Vladimir B Sulimov, Danil C Kutov, and Alexey V Sulimov. Advances in docking. Current medicinal chemistry, 26(42):7555–7580, 2019.
Li-Zhen Sun, Yangwei Jiang, Yuanzhe Zhou, and Shi-Jie Chen. Rldock: a new method for predicting rna–ligand interactions. Journal of chemical theory and computation, 16(11):7173–7183, 2020.
Andras Szilagyi and Yang Zhang. Template-based structure modeling of protein–protein interactions. Current opinion in structural biology, 24:10–23, 2014.
Xuan Tao, Yukun Huang, Chong Wang, Fang Chen, Lingling Yang, Li Ling, Zhenming Che, and Xianggui Chen. Recent developments in molecular docking technology applied in food science: a review. International Journal of Food Science & Technology, 55(1):33–45, 2020.
Richard D Taylor, Philip J Jewsbury, and Jonathan W Essex. A review of protein-small molecule docking methods. Journal of computer-aided molecular design, 16:151–166, 2002.
Reiji Teramoto and Hiroaki Fukunishi. Supervised consensus scoring for docking and virtual screening. Journal of chemical information and modeling, 47(2):526– 534, 2007.
Amy Hin Yan Tong, Becky Drees, Giuliano Nardelli, Gary D Bader, Barbara Brannetti, Luisa Castagnoli, Marie Evangelista, Silvia Ferracuti, Bryce Nelson, Serena Paoluzi, et al. A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science, 295(5553):321–324, 2002.
Weida Tong, William J Welsh, Leming Shi, Hong Fang, and Roger Perkins. Structure-activity relationship approaches and applications. Environmental Toxicology and Chemistry: An International Journal, 22(8):1680–1695, 2003.
Mieczyslaw Torchala, Iain H Moal, Raphael AG Chaleil, Juan Fernandez-Recio, and Paul A Bates. Swarmdock: a server for flexible protein–protein docking. Bioinformatics, 29(6):807–809, 2013.
Oleg Trott and Arthur J Olson. Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2):455–461, 2010.
Sadettin Y Ugurlu, David McDonald, Huangshu Lei, Alan M Jones, Shu Li, Henry Y Tong, Mark S Butler, and Shan He. Cobdock: an accurate and practical machine learning-based consensus blind docking method. Journal of Cheminformatics, 16(1):5, 2024. 51
Sandor Vajda, Dmitri Beglov, Amanda E Wakefield, Megan Egbert, and Adrian Whitty. Cryptic binding sites on proteins: definition, detection, and druggability. Current opinion in chemical biology, 44:1–8, 2018.
Ilya A Vakser. Protein-protein docking: From interaction to interactome. Biophysical journal, 107(8):1785–1793, 2014.
GCP Van Zundert, JPGLM Rodrigues, M Trellet, C Schmitz, PL Kastritis, E Karaca, ASJ Melquiond, Marc van Dijk, SJ De Vries, and AMJJ Bonvin. The haddock2. 2 web server: user-friendly integrative modeling of biomolecular complexes. Journal of molecular biology, 428(4):720–725, 2016.
Patrick ML Vanderheyden and Nerdjes Benachour. Influence of the cellular environment on ligand binding kinetics at membrane-bound targets. Bioorganic & Medicinal Chemistry Letters, 27(16):3621–3628, 2017.
Goutham N Vemuri and Aristos A Aristidou. Metabolic engineering in the-omics era: elucidating and modulating regulatory networks. Microbiology and Molecular Biology Reviews, 69(2):197–216, 2005.
Marcel L Verdonk, Jason C Cole, Michael J Hartshorn, Christopher W Murray, and Richard D Taylor. Improved protein–ligand docking using gold. Proteins: Structure, Function, and Bioinformatics, 52(4):609–623, 2003.
Marcel L Verdonk and Wijnand TM Mooij. Knowledge-based methods in structure-based design. In Computational and Structural Approaches to Drug Discovery, pages 111–126. 2007.
Jeffrey R Wagner, Christopher T Lee, Jacob D Durrant, Robert D Malmstrom, Victoria A Feher, and Rommie E Amaro. Emerging computational methods for the rational discovery of allosteric drugs. Chemical reviews, 116(11):6370–6390, 2016.
W Patrick Walters, Matthew T Stahl, and Mark A Murcko. Virtual screening—an overview. Drug discovery today, 3(4):160–178, 1998.
Cheng Wang, Wenyan Wang, Kun Lu, Jun Zhang, Peng Chen, and Bing Wang. Predicting drug-target interactions with electrotopological state fingerprints and amphiphilic pseudo amino acid composition. International Journal of Molecular Sciences, 21(16):5694, 2020.
Lirong Wang, Chao Ma, Peter Wipf, Haibin Liu, Weiwei Su, and Xiang-Qun Xie. Targethunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. The AAPS journal, 15:395–406, 2013.
Qi Wang, Mingyue Zheng, Zhimin Huang, Xinyi Liu, Huchen Zhou, Yingyi Chen, Ting Shi, and Jian Zhang. Toward understanding the molecular basis for chemical allosteric modulator design. Journal of Molecular Graphics and Modelling, 38:324–333, 2012.
Renxiao Wang, Yipin Lu, and Shaomeng Wang. Comparative evaluation of 11 scoring functions for molecular docking. Journal of medicinal chemistry, 46(12):2287– 2303, 2003.
Michael D Ward. Combining Computer Simulations and Deep Learning to Understand and Predict Protein Structural Dynamics. PhD thesis, Washington University in St. Louis, 2022.
Andrew Waterhouse, Martino Bertoni, Stefan Bienert, Gabriel Studer, Gerardo Tauriello, Rafal Gumienny, Florian T Heer, Tjaart A P de Beer, Christine Rempfer, Lorenza Bordoli, et al. Swiss-model: homology modelling of protein structures and complexes. Nucleic acids research, 46(W1):W296–W303, 2018.
Benjamin Webb and Andrej Sali. Comparative protein structure modeling using modeller. Current protocols in bioinformatics, 54(1):5–6, 2016.
David Weininger. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences, 28(1):31–36, 1988.
David Weininger, Arthur Weininger, and Joseph L Weininger. Smiles. 2. algorithm for generation of unique smiles notation. Journal of chemical information and computer sciences, 29(2):97–101, 1989.
Cody J Wenthur, Patrick R Gentry, Thomas P Mathews, and Craig W Lindsley. Drugs for allosteric sites on receptors. Annual review of pharmacology and toxicology, 54(1):165–184, 2014.
Michael R Wood, Corey R Hopkins, John T Brogan, P Jeffrey Conn, and Craig W Lindsley. “molecular switches” on mglur allosteric ligands that modulate modes of pharmacology. Biochemistry, 50(13):2403–2410, 2011.
Qi Wu, Zhenling Peng, Yang Zhang, and Jianyi Yang. Coach-d: improved protein– ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic acids research, 46(W1):W438–W442, 2018.
Arthur Wuster and M Madan Babu. Chemogenomics and biotechnology. Trends in biotechnology, 26(5):252–258, 2008.
Lei Xie, Li Xie, and Philip E Bourne. Structure-based systems biology for analyzing off-target binding. Current opinion in structural biology, 21(2):189–199, 2011.
Xianjin Xu, Marshal Huang, and Xiaoqin Zou. Docking-based inverse virtual screening: methods, applications, and challenges. Biophysics reports, 4:1–16, 2018.
Yumeng Yan, Huanyu Tao, Jiahua He, and Sheng-You Huang. The hdock server for integrated protein–protein docking. Nature protocols, 15(5):1829–1852, 2020.
Yumeng Yan, Zeyu Wen, Xinxiang Wang, and Sheng-You Huang. Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking. Proteins: Structure, Function, and Bioinformatics, 85(3):497–512, 2017.
Jae-Seong Yang, Sang Woo Seo, Sungho Jang, Gyoo Yeol Jung, and Sanguk Kim. Rational engineering of enzyme allosteric regulation through sequence evolution analysis. PLoS computational biology, 8(7):e1002612, 2012.
Jianyi Yang, Ambrish Roy, and Yang Zhang. Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 29(20):2588–2595, 2013.
Jinsol Yang, Minkyung Baek, and Chaok Seok. Galaxydock3: Protein–ligand docking that considers the full ligand conformational flexibility. Journal of Computational Chemistry, 40(31):2739–2748, 2019.
Su-Qing Yang, Qing Ye, Jun-Jie Ding, Ming-Zhu Yin, Ai-Ping Lu, Xiang Chen, Ting-Jun Hou, and Dong-Sheng Cao. Current advances in ligand-based target prediction. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(3):e1504, 2021.
Zhi-Jiang Yao, Jie Dong, Yu-Jing Che, Min-Feng Zhu, Ming Wen, Ning-Ning Wang, Shan Wang, Ai-Ping Lu, and Dong-Sheng Cao. Targetnet: a web service for predicting potential drug–target interaction profiling via multi-target sar models. Journal of computer-aided molecular design, 30:413–424, 2016.
Wen-Ling Ye, Chao Shen, Guo-Li Xiong, Jun-Jie Ding, Ai-Ping Lu, Ting-Jun Hou, and Dong-Sheng Cao. Improving docking-based virtual screening ability by integrating multiple energy auxiliary terms from molecular docking scoring. Journal of Chemical Information and Modeling, 60(9):4216–4230, 2020.
Shuangye Yin, Lada Biedermannova, Jiri Vondrasek, and Nikolay V Dokholyan. Medusascore: an accurate force field-based scoring function for virtual drug screening. Journal of chemical information and modeling, 48(8):1656–1662, 2008.
Calvin K Yip, Kazuyoshi Murata, Thomas Walz, David M Sabatini, and Seong A Kang. Structure of the human mtor complex i and its implications for rapamycin inhibition. Molecular cell, 38(5):768–774, 2010.
Hua Yu, Jianxin Chen, Xue Xu, Yan Li, Huihui Zhao, Yupeng Fang, Xiuxiu Li, Wei Zhou, Wei Wang, and Yonghua Wang. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PloS one, 7(5):e37608, 2012.
Yaxia Yuan, Jianfeng Pei, and Luhua Lai. Ligbuilder v3: a multi-target de novo drug design approach. Frontiers in chemistry, 8:142, 2020.
Jianming Zhang, Francisco J Adrian, Wolfgang Jahnke, Sandra W Cowan-Jacob, ´ Allen G Li, Roxana E Iacob, Taebo Sim, John Powers, Christine Dierks, Fangxian Sun, et al. Targeting bcr–abl by combining allosteric with atp-binding-site inhibitors. Nature, 463(7280):501–506, 2010.
Jing Zhang, Huajun Li, Yubo Zhang, Chaoran Zhao, Yizi Zhu, and Mei Han. Uncovering the pharmacological mechanism of stemazole in the treatment of neurodegenerative diseases based on a network pharmacology approach. International journal of molecular sciences, 21(2):427, 2020.
Mingzhen Zhang, Jun Zhao, and Jie Zheng. Molecular understanding of a potential functional link between antimicrobial and amyloid peptides. Soft Matter, 10(38):7425–7451, 2014.
Jingtian Zhao, Yang Cao, and Le Zhang. Exploring the computational methods for protein-ligand binding site prediction. Computational and structural biotechnology journal, 18:417–426, 2020.
Alex Zhavoronkov, Yan A Ivanenkov, Alex Aliper, Mark S Veselov, Vladimir A Aladinskiy, Anastasiya V Aladinskaya, Victor A Terentiev, Daniil A Polykovskiy, Maksim D Kuznetsov, Arip Asadulaev, et al. Deep learning enables rapid identification of potent ddr1 kinase inhibitors. Nature biotechnology, 37(9):1038–1040, 2019.
Shuangjia Zheng, Xin Yan, Yuedong Yang, and Jun Xu. Identifying structure–property relationships through smiles syntax analysis with self-attention mechanism. Journal of chemical information and modeling, 59(2):914–923, 2019.
Wenjun Zheng. Predicting cryptic ligand binding sites based on normal modes guided conformational sampling. Proteins: Structure, Function, and Bioinformatics, 89(4):416–426, 2021.
Pei Zhou, Bowen Jin, Hao Li, and Sheng-You Huang. Hpepdock: a web server for blind peptide–protein docking based on a hierarchical algorithm. Nucleic acids research, 46(W1):W443–W450, 2018.
Wei Zhou, Yonghua Wang, Aiping Lu, and Ge Zhang. Systems pharmacology in small molecular drug discovery. International journal of molecular sciences, 17(2):246, 2016.
Jintao Zhu, Zhonghui Gu, Jianfeng Pei, and Luhua Lai. Diffbind: A se (3) equivariant network for accurate full-atom semi-flexible protein-ligand docking. arXiv preprint arXiv:2311.15201, 2023.
Sadettin Y Ugurlu, David McDonald, and Shan He. Mef-allosite: An accurate and robust multimodel ensemble feature selection for the allosteric site identification model. Journal of Cheminformatics, 16(1):116, 2024.
Sadettin Y Ugurlu and R Enisoglu. Investigation of metallacages for cisplatin encapsulation using density functional theory (dft). OAJ Materials and Devices, 8, 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Sadettin Y Ugurlu

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License ( Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- To the extent transferable, copyright in and to the undersigned article is hereby assigned to Collaborating Academics and Open Access Journal Materials and Devices (ISSN: 2495-3911) for publication in the website of the journal and as part of a book (eventually a special volume) that could be produced in a printed and/or an electronic form.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- Figures, tables, and other information present in articles published in the OAJ Materials and Devices may be reused without permission, provided the citation of original article is made in figure's or table's caption.