Computational Drug Discovery on HIV Virus with a Customized LSTM Variational Autoencoder Deep Learning Architecture.

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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. Additionally, 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 dataset comprised SMILES-encoded compounds, which were utilized to train the LSTM Autoencoder. Remarkably, our model achieved a training accuracy of 91% with a dataset containing 1,377 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.This article is protected by copyright. All rights reserved.