Article ; Online: Computational drug discovery on human immunodeficiency virus with a customized long short-term memory variational autoencoder deep-learning architecture.
CPT: pharmacometrics & systems pharmacology
2023 Volume 13, Issue 2, Page(s) 308–316
Abstract: Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti-HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by HIV ... ...
| Abstract | Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti-HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by HIV underscores the necessity for computational drug discovery methods to identify novel therapies. This investigation specifically focused on employing a long short-term memory (LSTM) variational autoencoder deep-learning architecture for computational drug discovery in relation to HIV. Our data set comprised simplified molecular input line entry system (SMILES)-encoded compounds, which were used to train the LSTM autoencoder. Remarkably, our model achieved a training accuracy of 91%, with a data set containing 1377 compounds. Leveraging the generative model derived from the training phase, we generated potential new drugs for combating HIV and assessed their interaction with the virus using a previously developed artificial intelligence model. Lastly, we verified the drug likeliness of our computationally generated compounds in accordance with Lipinski's rule of five. Overall, our study presents a promising approach to computational drug discovery in the ongoing battle against HIV. |
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| MeSH term(s) | Humans ; Artificial Intelligence ; Deep Learning ; HIV ; Memory, Short-Term ; Drug Discovery/methods ; HIV Infections/drug therapy |
| Language | English |
| Publishing date | 2023-12-05 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2697010-7 |
| ISSN | 2163-8306 ; 2163-8306 |
| ISSN (online) | 2163-8306 |
| ISSN | 2163-8306 |
| DOI | 10.1002/psp4.13085 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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