AIMC Topic: Anti-HIV Agents

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Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.

BMJ open
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
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...

Optimal STI controls for HIV patients based on an efficient deep Q learning method.

Journal of theoretical biology
We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). Structured treatment interruption (STI) is a regimen in which therapeutic drugs are periodically admi...

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study.

AIDS care
Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting f...

Improving HIV preexposure prophylaxis uptake with artificial intelligence and automation: a systematic review.

AIDS (London, England)
OBJECTIVES: To identify studies promoting the use of artificial intelligence (AI) or automation with HIV preexposure prophylaxis (PrEP) care and explore ways for AI to be used in PrEP interventions.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

NPJ systems biology and applications
Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Com...

A knowledge-guided pre-training framework for improving molecular representation learning.

Nature communications
Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised...

Industrializing AI/ML during the end-to-end drug discovery process.

Current opinion in structural biology
Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several ar...

Evaluation of the pharmacokinetic drug-drug interaction between the antiretroviral agents fostemsavir and maraviroc: a single-sequence crossover study in healthy participants.

HIV research & clinical practice
BACKGROUND: Fostemsavir is an oral prodrug of temsavir, a first-in-class attachment inhibitor that binds HIV-1 gp120, preventing initial HIV attachment and entry into host immune cells.

Switching to a NRTI-free 2 drug regimen (2DR) -a sub-analysis of the 48 weeks DUALIS study on metabolic and renal changes.

HIV research & clinical practice
Switching from a three-drug regimen (3DR: boosted darunavir [bDRV] and two nucleoside reverse transcriptase inhibitors [NRTIs]) to a two-drug regimen (2DR: bDRV and dolutegravir [DTG]) demonstrated non-inferiority with regard to viral suppression in...