Author

Parul Srivastava

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Sep 2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

Ashish Srivastava, Mohit Prajapati, Parul Srivastava et al. · Current pharmaceutical desig... · 0 citations

Review Jul 2026

A Survey Review of Computational Models and Their Applications in Predicting Drug-Drug Interactions

Drug-drug interactions (DDIs) represent a substantial challenge in contemporary pharmacotherapy, especially given polypharmacy, the effects of foods, and the modification of host-microbiota systems on drugs. Although useful, existing DDI identification techniques have many constraints related to cost, time, and scalability. A literature review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore, focusing on machine learning techniques, deep neural architectures, and network-based models that integrate multi-omic, pharmacological, and clinical data. By combining chemical, biological, and clinical data into scalable computer platforms, demonstrated that artificial intelligence techniques, such as machine learning (ML) and deep learning (DL), are changing the prediction of DDI. Some notable studies, such as DeepDDI, TP-DDI, and Decagon, use approaches that successfully capture the intricate PK-PD interactions of pharmaceuticals. On the other hand, food-drug interactions and microbiome-mediated drug interactions were also successfully predicted using multimodal and graph-based models, respectively. Critical issues, such as insufficient data, class imbalance, and model interpretability, must be addressed through explainable AI and multimodal fusion techniques. The purpose of this article is to present an overview of how artificial intelligence might serve not only as a tool but also as a strategic solution for safe prescribing and tailored pharmacotherapy, hence opening up new avenues for the field of drug safety science.

D. Tripathi, Ankita Wal, Vivek Kumar Gupta et al. · Current Bioinformatics · 0 citations