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Journal articles on the topic 'Drug design de novo'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Schneider, Gisbert. "Future De Novo Drug Design." Molecular Informatics 33, no. 6-7 (2014): 397–402. http://dx.doi.org/10.1002/minf.201400034.

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2

Kaalia, Rama, Ashwin Srinivasan, Amit Kumar, and Indira Ghosh. "ILP-assisted de novo drug design." Machine Learning 103, no. 3 (2016): 309–41. http://dx.doi.org/10.1007/s10994-016-5556-x.

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3

Grant, Lauren L., and Clarissa S. Sit. "De novo molecular drug design benchmarking." RSC Medicinal Chemistry 12, no. 8 (2021): 1273–80. http://dx.doi.org/10.1039/d1md00074h.

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Deep neural networks (DNNs) used for de novo drug design have different architectures and hyperparameters that impact the final output of suggested drug candidates. Herein we review benchmarking platforms that assess the utility and validity of DNNs.

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4

Lin, Eugene, Chieh-Hsin Lin, and Hsien-Yuan Lane. "Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design." Molecules 25, no. 14 (2020): 3250. http://dx.doi.org/10.3390/molecules25143250.

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A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we re

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5

Schneider, Gisbert. "ChemInform Abstract: Future De Novo Drug Design." ChemInform 45, no. 42 (2014): no. http://dx.doi.org/10.1002/chin.201442291.

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6

Zhang, Changsheng, and Luhua Lai. "Towards structure-based protein drug design." Biochemical Society Transactions 39, no. 5 (2011): 1382–86. http://dx.doi.org/10.1042/bst0391382.

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Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more

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7

Nicolaou, Christos A., Joannis Apostolakis, and Costas S. Pattichis. "De Novo Drug Design Using Multiobjective Evolutionary Graphs." Journal of Chemical Information and Modeling 49, no. 2 (2009): 295–307. http://dx.doi.org/10.1021/ci800308h.

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8

Popova, Mariya, Olexandr Isayev, and Alexander Tropsha. "Deep reinforcement learning for de novo drug design." Science Advances 4, no. 7 (2018): eaap7885. http://dx.doi.org/10.1126/sciadv.aap7885.

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9

Fischer, Thomas, Silvia Gazzola, and Rainer Riedl. "Approaching Target Selectivity by De Novo Drug Design." Expert Opinion on Drug Discovery 14, no. 8 (2019): 791–803. http://dx.doi.org/10.1080/17460441.2019.1615435.

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10

Hartenfeller, Markus, and Gisbert Schneider. "Enabling future drug discovery by de novo design." WIREs Computational Molecular Science 1, no. 5 (2011): 742–59. http://dx.doi.org/10.1002/wcms.49.

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11

Gupta, Anvita, Alex T. Müller, Berend J. H. Huisman, Jens A. Fuchs, Petra Schneider, and Gisbert Schneider. "Generative Recurrent Networks for De Novo Drug Design." Molecular Informatics 37, no. 1-2 (2017): 1700111. http://dx.doi.org/10.1002/minf.201700111.

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12

Rotstein, Sergio H., and Mark A. Murcko. "GenStar: A method for de novo drug design." Journal of Computer-Aided Molecular Design 7, no. 1 (1993): 23–43. http://dx.doi.org/10.1007/bf00141573.

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13

Nicolaou, C., C. Kannas, and E. Loizidou. "Multi-Objective Optimization Methods in De Novo Drug Design." Mini-Reviews in Medicinal Chemistry 12, no. 10 (2012): 979–87. http://dx.doi.org/10.2174/138955712802762284.

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14

Amaravadhi, Harikishore, Kwanghee Baek, and Ho Yoon. "Revisiting De Novo Drug Design: Receptor Based Pharmacophore Screening." Current Topics in Medicinal Chemistry 14, no. 16 (2014): 1890–98. http://dx.doi.org/10.2174/1568026614666140929115506.

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15

Skalic, Miha, José Jiménez, Davide Sabbadin, and Gianni De Fabritiis. "Shape-Based Generative Modeling for de Novo Drug Design." Journal of Chemical Information and Modeling 59, no. 3 (2019): 1205–14. http://dx.doi.org/10.1021/acs.jcim.8b00706.

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16

Yuan, Yaxia, Jianfeng Pei, and Luhua Lai. "LigBuilder 2: A Practical de Novo Drug Design Approach." Journal of Chemical Information and Modeling 51, no. 5 (2011): 1083–91. http://dx.doi.org/10.1021/ci100350u.

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17

Schneider, Gisbert, Kimito Funatsu, Yasushi Okuno, and Dave Winkler. "De novo Drug Design - Ye olde Scoring Problem Revisited." Molecular Informatics 36, no. 1-2 (2017): 1681031. http://dx.doi.org/10.1002/minf.201681031.

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18

Gupta, Anvita, Alex T. Müller, Berend J. H. Huisman, Jens A. Fuchs, Petra Schneider, and Gisbert Schneider. "Erratum: Generative Recurrent Networks for De Novo Drug Design." Molecular Informatics 37, no. 1-2 (2018): 1880141. http://dx.doi.org/10.1002/minf.201880141.

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19

Schneider, Gisbert, and Uli Fechner. "Computer-based de novo design of drug-like molecules." Nature Reviews Drug Discovery 4, no. 8 (2005): 649–63. http://dx.doi.org/10.1038/nrd1799.

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20

Xia, Xiaolin, Jianxing Hu, Yanxing Wang, Liangren Zhang, and Zhenming Liu. "Graph-based generative models for de Novo drug design." Drug Discovery Today: Technologies 32-33 (December 2019): 45–53. http://dx.doi.org/10.1016/j.ddtec.2020.11.004.

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21

Dean, P. M. "Chemical genomics: a challenge for de novo drug design." Molecular Biotechnology 37, no. 3 (2007): 237–45. http://dx.doi.org/10.1007/s12033-007-0037-x.

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22

Devi, R. Vasundhara, S. Siva Sathya, and Mohane Selvaraj Coumar. "Evolutionary algorithms for de novo drug design – A survey." Applied Soft Computing 27 (February 2015): 543–52. http://dx.doi.org/10.1016/j.asoc.2014.09.042.

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23

Xiong, Jiacheng, Zhaoping Xiong, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng. "Graph neural networks for automated de novo drug design." Drug Discovery Today 26, no. 6 (2021): 1382–93. http://dx.doi.org/10.1016/j.drudis.2021.02.011.

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24

Masek, Brian B., David S. Baker, Roman J. Dorfman, et al. "Multistep Reaction Based De Novo Drug Design: Generating Synthetically Feasible Design Ideas." Journal of Chemical Information and Modeling 56, no. 4 (2016): 605–20. http://dx.doi.org/10.1021/acs.jcim.5b00697.

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25

Barrawaz, Aateka Y. "COMPUTER AIDED DRUG DESIGN: A MINI-REVIEW." Journal of Medical Pharmaceutical And Allied Sciences 9, no. 5 (2020): 2584–91. http://dx.doi.org/10.22270/jmpas.v9i5.971.

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New drug discovery and development process is considered much complex process which is time consuming and resources accommodating too. So computer aided drug design are being broadly used to enhance the effectiveness of the drug discovery and development process which ultimately saves time and resources. Various approaches to Computer aided drug design are evaluated to shows potential techniques in accordance with their needs. Two approaches are considered to designing of drug first one is structure-based and second one is Ligand based drug designs. In this review, we are discussing about high

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26

Hessler, Gerhard, and Karl-Heinz Baringhaus. "Artificial Intelligence in Drug Design." Molecules 23, no. 10 (2018): 2520. http://dx.doi.org/10.3390/molecules23102520.

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Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. S

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27

Shimada, Jun, Sean Ekins, Carl Elkin, Eugene I. Shakhnovich, and Jean-Pierre Wery. "Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs." TARGETS 1, no. 6 (2002): 196–205. http://dx.doi.org/10.1016/s1477-3627(02)02274-2.

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28

Gálvez, J., R. García-Domenech, C. de Gregorio Alapont, J. V. de Julián-Ortiz, and L. Popa. "Pharmacological distribution diagrams: A tool for de novo drug design." Journal of Molecular Graphics 14, no. 5 (1996): 272–76. http://dx.doi.org/10.1016/s0263-7855(96)00081-1.

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29

Blaschke, Thomas, Josep Arús-Pous, Hongming Chen, et al. "REINVENT 2.0: An AI Tool for De Novo Drug Design." Journal of Chemical Information and Modeling 60, no. 12 (2020): 5918–22. http://dx.doi.org/10.1021/acs.jcim.0c00915.

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30

Rodrigues, Tiago, Daniel Reker, Martin Welin, et al. "De Novo Fragment Design for Drug Discovery and Chemical Biology." Angewandte Chemie International Edition 54, no. 50 (2015): 15079–83. http://dx.doi.org/10.1002/anie.201508055.

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31

Schneider, Gisbert, and David E. Clark. "Automated De Novo Drug Design: Are We Nearly There Yet?" Angewandte Chemie International Edition 58, no. 32 (2019): 10792–803. http://dx.doi.org/10.1002/anie.201814681.

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32

Schneider, Gisbert, and David E. Clark. "Automated De Novo Drug Design: Are We Nearly There Yet?" Angewandte Chemie 131, no. 32 (2019): 10906–17. http://dx.doi.org/10.1002/ange.201814681.

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33

Pellegrini, Eric, and Martin J. Field. "Development and testing of a de novo drug-design algorithm." Journal of Computer-Aided Molecular Design 17, no. 10 (2003): 621–41. http://dx.doi.org/10.1023/b:jcam.0000017362.66268.d5.

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34

Rotstein, Sergio H., and Mark A. Murcko. "GroupBuild: a fragment-based method for de novo drug design." Journal of Medicinal Chemistry 36, no. 12 (1993): 1700–1710. http://dx.doi.org/10.1021/jm00064a003.

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35

Rotstein, SergioH, and MarkA Murcko. "GroupBuild: a fragment-based method for de novo drug design." Journal of Molecular Graphics 12, no. 1 (1994): 78. http://dx.doi.org/10.1016/0263-7855(94)80069-3.

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36

Gasteiger, Johann. "De novo design and synthetic accessibility." Journal of Computer-Aided Molecular Design 21, no. 6 (2007): 307–9. http://dx.doi.org/10.1007/s10822-007-9115-1.

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37

Kim, Jintae, Sera Park, Dongbo Min, and Wankyu Kim. "Comprehensive Survey of Recent Drug Discovery Using Deep Learning." International Journal of Molecular Sciences 22, no. 18 (2021): 9983. http://dx.doi.org/10.3390/ijms22189983.

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Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between

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38

Mouchlis, Varnavas D., Antreas Afantitis, Angela Serra, et al. "Advances in De Novo Drug Design: From Conventional to Machine Learning Methods." International Journal of Molecular Sciences 22, no. 4 (2021): 1676. http://dx.doi.org/10.3390/ijms22041676.

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De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learnin

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39

Mutter, M., K. H. Altmann, G. Tuchscherer, and S. Vuilleumier. "Strategies for the de novo design of proteins." Tetrahedron 44, no. 3 (1988): 771–85. http://dx.doi.org/10.1016/s0040-4020(01)86116-0.

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40

Ståhl, Niclas, Göran Falkman, Alexander Karlsson, Gunnar Mathiason, and Jonas Boström. "Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design." Journal of Chemical Information and Modeling 59, no. 7 (2019): 3166–76. http://dx.doi.org/10.1021/acs.jcim.9b00325.

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41

Krishnan, Sowmya Ramaswamy, Navneet Bung, Gopalakrishnan Bulusu, and Arijit Roy. "Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning." Journal of Chemical Information and Modeling 61, no. 2 (2021): 621–30. http://dx.doi.org/10.1021/acs.jcim.0c01060.

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42

Li, Bowen, Zhefan Yuan, Priyesh Jain, et al. "De novo design of functional zwitterionic biomimetic material for immunomodulation." Science Advances 6, no. 22 (2020): eaba0754. http://dx.doi.org/10.1126/sciadv.aba0754.

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Superhydrophilic zwitterionic polymers are a class of nonfouling materials capable of effectively resisting any nonspecific interactions with biological systems. We designed here a functional zwitterionic polymer that achieves a trade-off between nonspecific interactions providing the nonfouling property and a specific interaction for bioactive functionality. Built from phosphoserine, an immune-signaling molecule in nature, this zwitterionic polymer exhibits both nonfouling and immunomodulatory properties. Its conjugation to uricase is shown to proactively eradicate all unwanted immune respons

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43

Alberts, Ian L., Nikolay P. Todorov, and Philip M. Dean. "Receptor Flexibility in de Novo Ligand Design and Docking." Journal of Medicinal Chemistry 48, no. 21 (2005): 6585–96. http://dx.doi.org/10.1021/jm050196j.

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44

Grisoni, Francesca, Berend J. H. Huisman, Alexander L. Button, et al. "Combining generative artificial intelligence and on-chip synthesis for de novo drug design." Science Advances 7, no. 24 (2021): eabg3338. http://dx.doi.org/10.1126/sciadv.abg3338.

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Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfull

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45

Grisoni, Francesca, and Gisbert Schneider. "De novo Molecular Design with Generative Long Short-term Memory." CHIMIA International Journal for Chemistry 73, no. 12 (2019): 1006–11. http://dx.doi.org/10.2533/chimia.2019.1006.

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Drug discovery benefits from computational models aiding the identification of new chemical matter with bespoke properties. The field of de novo drug design has been particularly revitalized by adaptation of generative machine learning models from the field of natural language processing. These deep neural network models are trained on recognizing molecular structures and generate new molecular entities without relying on pre-determined sets of molecular building blocks and chemical transformations for virtual molecule construction. Implicit representation of chemical knowledge provides an alt

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46

Banjare, Laxmi, Sant Kumar Verma, Akhlesh Kumar Jain, and Suresh Thareja. "Lead Molecules as Novel Aromatase Inhibitors: In Silico De Novo Designing and Binding Affinity Studies." Letters in Drug Design & Discovery 17, no. 5 (2020): 655–65. http://dx.doi.org/10.2174/1570180816666190703152659.

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Background:Aromatase inhibitors emerged as a pivotal moiety to selectively block estrogen production, prevention and treatment of tumour growth in breast cancer. De novo drug design is an alternative approach to blind virtual screening for successful designing of the novel molecule against various therapeutic targets.Objective:In the present study, we have explored the de novo approach to design novel aromatase inhibitors.Method:The e-LEA3D, a computational-aided drug design web server was used to design novel drug-like candidates against the target aromatase. For drug-likeness ADME parameters

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47

BORMAN, STU. "New 3-D Search and De Novo Design Techniques Aid Drug Development." Chemical & Engineering News 70, no. 32 (1992): 18–26. http://dx.doi.org/10.1021/cen-v070n032.p018.

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48

Clark, David E., Mike A. Firth, and Christopher W. Murray. "MOLMAKER: De Novo Generation of 3D Databases for Use in Drug Design." Journal of Chemical Information and Computer Sciences 36, no. 1 (1996): 137–45. http://dx.doi.org/10.1021/ci9502055.

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49

Takeda, Shunichi, Hiromasa Kaneko, and Kimito Funatsu. "Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules." Journal of Chemical Information and Modeling 56, no. 10 (2016): 1885–93. http://dx.doi.org/10.1021/acs.jcim.6b00038.

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50

Rodrigues, Tiago, Takayuki Kudoh, Filip Roudnicky, et al. "Steering Target Selectivity and Potency by Fragment-Based De Novo Drug Design." Angewandte Chemie International Edition 52, no. 38 (2013): 10006–9. http://dx.doi.org/10.1002/anie.201304847.

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