AIMC Topic: Anti-HIV Agents
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Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.
Scientific reports
Aug 9, 2025
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...
Role of eccentricity based topological descriptors to predict anti-HIV drugs attributes with supervised machine learning algorithms.
Computers in biology and medicine
Mar 27, 2025
Chemical graphs are mathematical representations of molecular structures, where atoms are represented as vertices, while chemical bonds are depicted as edges of a graph. The chemical graphs are widely used in cheminformatics to analyze molecular prop...
Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.
Frontiers in cellular and infection microbiology
Mar 11, 2025
BACKGROUND: Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individuals fail to achieve adequate immune reconstitution, known as immunological nonresponse (INR), which i...
Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.
AIDS (London, England)
Feb 24, 2025
OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model ...
A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 10, 2025
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug ...
[Incidence and determinants of viral load rebound in people receiving multi-month dispensing of antiretroviral therapy at the Regional Annex Hospital of Dschang from 2018-2023].
The Pan African medical journal
Nov 12, 2024
INTRODUCTION: in Cameroon, multi-month dispensing (MMD) of antiretrovirals (ARVs) was introduced to improve treatment adherence among people living with HIV (PLHIV). However, this strategy has limitations that may lead to viral load rebound. The purp...
In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method.
Nature communications
Nov 7, 2024
Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which ...
Pre-training strategy for antiviral drug screening with low-data graph neural network: A case study in HIV-1 K103N reverse transcriptase.
Journal of computational chemistry
Oct 22, 2024
Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hindered by limited datasets. To address this limitation, we introduced a robust GNN training framework...
Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.
BMJ open
Sep 24, 2024
OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The alg...
SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.
Journal of chemical information and modeling
Aug 23, 2024
Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into...
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