Artificial intelligence for drug repurposing against infectious diseases

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(2), P. 100071 - 100071

Published: June 12, 2024

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated repurposing. AI allows researchers analyze massive datasets, revealing hidden connections between existing drugs, disease targets, potential treatments. This approach boasts several advantages. First, repurposing drugs leverages established safety data reduces development time costs. Second, can broaden search for effective therapies by identifying unexpected new targets. Finally, help mitigate limitations predicting minimizing side effects, optimizing repurposing, navigating intellectual property hurdles. The article explores specific strategies like virtual screening, target identification, structure base design natural language processing. Real-world examples highlight AI-driven in discovering treatments

Language: Английский

---

Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences

AI, Journal Year: 2025, Volume and Issue: 6(1), P. 4 - 4

Published: Jan. 2, 2025

Background: COVID-19 genetic sequence research is crucial despite immunizations and pandemic control. COVID-19-causing SARS-CoV-2 must be understood genomically for several reasons. New viral strains may resist vaccines. Categorizing sequences helps researchers track changes assess immunization efficacy. Classifying genome with other viruses to understand its evolution interactions illnesses. Methods: The proposed study introduces a deep learning-based genomic categorization approach. Attention-based hybrid learning (DL) models categorize 1423 11,388 sequences. An unknown dataset also used the models. five models’ accuracy, f1-score, area under curve (AUC), precision, Matthews correlation coefficient (MCC), recall are evaluated. Results: results indicate that Convolutional neural network (CNN) Bidirectional long short-term memory (BLSTM) attention layer (CNN-BLSTM-Att) achieved an accuracy of 99.99%, which outperformed For external validation, model shows 99.88%. It reveals DL-based approaches can accurately classify high degree accuracy. This method might assist in identifying classifying virus clinical situations. Immunizations have lowered danger, but categorizing global health activities plan recurrence or future threats.

Language: Английский

---

Use and dual use of synthetic biology

Comptes Rendus Biologies, Journal Year: 2025, Volume and Issue: 348(G1), P. 71 - 88

Published: March 7, 2025

Language: Английский

---

Sensing Techniques for Microbial Pathogens

Published: Jan. 1, 2025

Language: Английский

---