Recent advances and application of generative adversarial networks in drug discovery, development, and targeting
A rising amount of research demonstrates that artificial intelligence and machine learning approaches can provide an essential basis for the drug design and discovery process. Deep learning algorithms are being developed in response to recent advances in computer technology as part of the creation o...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Artificial Intelligence in the Life Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318522000150 |
| _version_ | 1811197646778400768 |
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| author | Satvik Tripathi Alisha Isabelle Augustin Adam Dunlop Rithvik Sukumaran Suhani Dheer Alex Zavalny Owen Haslam Thomas Austin Jacob Donchez Pushpendra Kumar Tripathi Edward Kim |
| author_facet | Satvik Tripathi Alisha Isabelle Augustin Adam Dunlop Rithvik Sukumaran Suhani Dheer Alex Zavalny Owen Haslam Thomas Austin Jacob Donchez Pushpendra Kumar Tripathi Edward Kim |
| author_sort | Satvik Tripathi |
| collection | DOAJ |
| description | A rising amount of research demonstrates that artificial intelligence and machine learning approaches can provide an essential basis for the drug design and discovery process. Deep learning algorithms are being developed in response to recent advances in computer technology as part of the creation of therapeutically relevant medications for the treatment of a variety of ailments. In this review, we focus on the most recent advances in the areas of drug design and discovery research employing generative deep learning methodologies such as generative adversarial network (GAN) frameworks. To begin, we examine drug design and discovery studies that use several GAN methodologies to evaluate one key application, such as molecular de novo design in drug design and discovery. Furthermore, we discuss many GAN models for dimension reduction of single-cell data at the preclinical stage of the drug development pipeline. We also show various experiments in de novo peptide and protein creation utilizing GAN frameworks. Furthermore, we discuss the limits of past drug design and discovery research employing GAN models. Finally, we give a discussion on future research prospects and obstacles. |
| first_indexed | 2024-04-12T01:17:12Z |
| format | Article |
| id | doaj.art-fec86ab07b51427f8ef564d2b8f8b932 |
| institution | Directory Open Access Journal |
| issn | 2667-3185 |
| language | English |
| last_indexed | 2024-04-12T01:17:12Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence in the Life Sciences |
| spelling | doaj.art-fec86ab07b51427f8ef564d2b8f8b9322022-12-22T03:53:55ZengElsevierArtificial Intelligence in the Life Sciences2667-31852022-12-012100045Recent advances and application of generative adversarial networks in drug discovery, development, and targetingSatvik Tripathi0Alisha Isabelle Augustin1Adam Dunlop2Rithvik Sukumaran3Suhani Dheer4Alex Zavalny5Owen Haslam6Thomas Austin7Jacob Donchez8Pushpendra Kumar Tripathi9Edward Kim10Corresponding author.; College of Computing and Informatics Drexel University Philadelphia, PA 19104 USACollege of Engineering Drexel University Philadelphia, USACollege of Arts and Sciences Drexel University Philadelphia, PA 19104 USACollege of Computing and Informatics Drexel University Philadelphia, PA 19104 USACollege of Arts and Sciences Drexel University Philadelphia, PA 19104 USACollege of Computing and Informatics Drexel University Philadelphia, PA 19104 USACollege of Arts and Sciences Drexel University Philadelphia, PA 19104 USACollege of Engineering Drexel University Philadelphia, PA 19104 USACollege of Biomedcial Engineering Drexel University Philadelphia, PA 19104 USAInstitute of Pharmaceutical Sciences University of Lucknow Lucknow, IndiaCollege of Computing and Informatics Drexel University Philadelphia, PA 19104 USAA rising amount of research demonstrates that artificial intelligence and machine learning approaches can provide an essential basis for the drug design and discovery process. Deep learning algorithms are being developed in response to recent advances in computer technology as part of the creation of therapeutically relevant medications for the treatment of a variety of ailments. In this review, we focus on the most recent advances in the areas of drug design and discovery research employing generative deep learning methodologies such as generative adversarial network (GAN) frameworks. To begin, we examine drug design and discovery studies that use several GAN methodologies to evaluate one key application, such as molecular de novo design in drug design and discovery. Furthermore, we discuss many GAN models for dimension reduction of single-cell data at the preclinical stage of the drug development pipeline. We also show various experiments in de novo peptide and protein creation utilizing GAN frameworks. Furthermore, we discuss the limits of past drug design and discovery research employing GAN models. Finally, we give a discussion on future research prospects and obstacles.http://www.sciencedirect.com/science/article/pii/S2667318522000150Generative adversarial networksMachine learningArtificial intelligencePharmacologyDrug discoveryDrug targeting |
| spellingShingle | Satvik Tripathi Alisha Isabelle Augustin Adam Dunlop Rithvik Sukumaran Suhani Dheer Alex Zavalny Owen Haslam Thomas Austin Jacob Donchez Pushpendra Kumar Tripathi Edward Kim Recent advances and application of generative adversarial networks in drug discovery, development, and targeting Artificial Intelligence in the Life Sciences Generative adversarial networks Machine learning Artificial intelligence Pharmacology Drug discovery Drug targeting |
| title | Recent advances and application of generative adversarial networks in drug discovery, development, and targeting |
| title_full | Recent advances and application of generative adversarial networks in drug discovery, development, and targeting |
| title_fullStr | Recent advances and application of generative adversarial networks in drug discovery, development, and targeting |
| title_full_unstemmed | Recent advances and application of generative adversarial networks in drug discovery, development, and targeting |
| title_short | Recent advances and application of generative adversarial networks in drug discovery, development, and targeting |
| title_sort | recent advances and application of generative adversarial networks in drug discovery development and targeting |
| topic | Generative adversarial networks Machine learning Artificial intelligence Pharmacology Drug discovery Drug targeting |
| url | http://www.sciencedirect.com/science/article/pii/S2667318522000150 |
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