2026· Journal of Pharmaceutical Research and Innovation· 0 citations
TL;DR
It is concluded that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.
Abstract
Antimicrobial resistance (AMR) has emerged as a major global health challenge, necessitating the development of innovative strategies to accelerate antibiotic drug discovery. Traditional drug development approaches are often time-consuming, costly, and resource-intensive, creating a growing need for computational methods that can improve research efficiency and therapeutic candidate identification. This study investigates the role of computational protein structure prediction in advancing antibiotic drug discovery through a conceptual literature review of recent research published between 2022 and 2026. The review examines the application of protein structure prediction techniques, including deep learning, machine learning, molecular simulations, and AlphaFold-based approaches, in supporting structure-based drug design. Particular attention is given to their contributions in drug target identification, binding site prediction, protein–ligand interaction analysis, virtual screening, and candidate prioritization. The findings indicate that computational approaches significantly enhance the efficiency of early-stage drug discovery by enabling accurate structural modeling, rapid screening of large compound libraries, and improved identification of promising antibacterial targets. Furthermore, the integration of artificial intelligence with molecular modeling techniques has strengthened prediction accuracy and facilitated the discovery of novel therapeutic candidates against drug-resistant pathogens. Despite these advancements, challenges related to prediction reliability, biological complexity, computational resource requirements, and dependence on high-quality datasets continue to affect the robustness of current approaches. Additionally, the reviewed studies emphasize the necessity of experimental validation to confirm computational findings and ensure clinical applicability. Overall, the study concludes that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.
This review highlights how artificial intelligence bridges bacterial genomics and antimicrobial drug discovery and offers a strategic framework for the fast-tracked, cost-effective prioritization of therapeutic targets, making it a vital resource for tackling emerging pathogens.
Thayssa de Oliveira Teixeira, Ruana Carolina Cabral da Silva, M. Alves et al.· Journal of Computer-Aided Mo...· 1 citation
Antimicrobial resistance (AMR) is a critical global health problem that has become increasingly alarming in recent years. The discovery of new antibiotics is one approach for alleviating AMR, however, screening for novel drugs is time consuming and expensive. To accelerate antibiotic discovery, the integration of machine learning algorithms with Quantitative Structure–Activity Relationship (QSAR) calculations could provide a rapid solution. Thus, this study combines a QSAR model and machine learning algorithms to predict antibacterial activities of potential novel drugs based on chemical information. Information on compounds that are reportedly active and inactive against bacteria was downloaded from the PubChem database and manually curated to create positive and negative datasets. The decision tree (DT), support vector machine (SVM), and naïve Bayesian (NB) algorithms were employed to predict the antibacterial activities of chemical compounds from their Simplified Molecular Input Line Entry System (SMILES) information. The models were then evaluated quantitatively and tuned. DT and SVM exhibited comparable predictive performance and outperformed the NB model, achieving accuracy, precision, sensitivity, and AUC-ROC values exceeding 0.90. DT was chosen for further analysis because of its simplicity and effectiveness. This revealed that descriptors relating to the electrotopology and β-lactam structures of compounds were the top contributors to model predictability. The model was then further tested against different classes of antibiotics and achieved high accuracy in all classes. The model is freely available as a web application at: https://antibacterial-predictor-model-ocogzqyibervrqb7trvfev.streamlit.app/.
Jiratchaya Nakbang, Chonthicha Arbsuwan, S. Prom-on et al.· PLOS Digital Health· 0 citations
Traditional drug development suffers from high costs, low success rates, and patient response variability. Precision drug discovery seeks to overcome these limitations by targeting specific genetic and molecular mechanisms but faces challenges in integrating cross-scale, multimodal biomedical data. Recent advances in artificial intelligence (AI), especially deep learning, provide powerful tools to navigate these complexities. This review surveys representative AI methods across four core stages of precision drug discovery: (a) target identification and validation using omics-, drug-, and structure-based approaches; (b) structure- and sequence-guided virtual screening of active compounds; (c) individualized drug response prediction integrating cell-line, single-cell, and multimodal data; and (d) AI-enabled toxicology and safety modeling to anticipate adverse liabilities and improve translational success. We further highlight a paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance. Finally, we discuss persistent challenges, including data bias, limited interpretability, and in silico-to-wet lab translation.
Unknown authors· Annual Review of Pharmacolog...· 0 citations
The rapid emergence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) has significantly reduced the effectiveness of conventional therapeutic regimens necessitating the discovery of novel drug targets and inhibitors. Recent bioinformatics-driven approaches for identifying putative inhibitors targeting essential mycobacterial proteins are comprehensively reviewed. Unlike previous reviews that tend to focus either on drug resistance mechanisms or drug discovery using computational approaches separately, this review integrates both aspects by linking genetic mutations associated with drug resistance and advanced computational approaches for anti-TB drug discovery. Integrative computational strategies including subtractive genomics, molecular docking, molecular dynamics simulations and machine learning-based prioritisation are emphasised. These approaches enable the identification of pathogen specific targets with minimal homology with the host proteins. This review further highlights the importance of natural products, peptides and drug repurposing strategies in targeting MDR-TB. Computational pipelines have shown the potential to greatly speed up early-stage drug discovery while lowering related costs and time, despite the challenges. The benefits of combining multi-omics data with artificial intelligence to create strain-specific treatment approaches are further demonstrated by case-based insights. Furthermore, this review highlights the current challenges in translating computational predictions into experimental and clinical validation while providing future directions including AI/ML based drug discovery, network pharmacology, host-directed therapies, and personalized medicine. Overall, this review underscores the critical role of bioinformatics in addressing the global burden of MDR-TB and highlights its transformative role in guiding next-generation anti-TB drug development. Not applicable.
Elizabeth Annie George, Mahima Senthilkumar, Kavitha Thirupugazh et al.· Beni-Suef University Journal...· 0 citations
Viral outbreaks combined with rapid emergence of mutated viruses have highlighted an urging need for accelerating antiviral drug discovery pipelines. Unfortunately, current drug discovery remains stuck to conventional methods which are slow especially during pandemics. In this article, we present a literature-based synthesis of an integrative machine learning (ML) guided QSAR framework that unifies ligand-based, structure-based, and systems biology approaches towards the aim of generating a host directed antiviral repurposing strategy. Moreover, a modern ML enhanced QSAR modeling strategy is proposed to target host directed therapeutics (HDTs), particularly the host kinase enzymes. The proposed framework integrates molecular descriptor modeling, ensemble learning methods (e.g., RF, gradient boosting), graph neural networks (GNNs), and multi-omics target prioritization to outline a predictive antiviral repurposing model. This structured workflow encompasses dataset assembly, descriptor generation, model training, virtual screening, and experimental validation as sequential stages to guide, rather than as a pipeline that has itself been built or independently validated here, translational deployment. The review is illustrated through a retrospective narrative synthesis of four independently published, clinically relevant repurposed HDTs, namely Baricitinib, Lapatinib, Bemcentinib, and Sunitinib. These published case studies, drawn from the primary literature, exemplify how AI/ML-enhanced QSAR and network-based approaches have been used elsewhere to identify active antiviral kinase inhibitors; they are presented here as illustrative evidence of feasibility of such a computational pipeline. Thus, the AI guided repurposing of host kinase inhibitors offers a systematically accelerated strategy to bridge the drug design gap, with the potential for faster therapeutic deployment against viral threats pending prospective, harmonized validation. This review describes a framework that combines artificial intelligence (AI), machine learning (ML), and Quantitative Structure-Activity Relationship (QSAR) modeling to speed up the search for new antiviral drugs. Instead of targeting the virus directly, the framework targets host cell proteins such as kinases, which many viruses hijack during infection, an approach also known as host-directed therapy (HDT). To show how this approach could work, we review four drugs that were originally developed for other diseases and later found to also fight viral infections: Baricitinib, Lapatinib, Bemcentinib, and Sunitinib. Each case was reported independently in the published literature, and we present them here as examples of what AI-assisted drug repurposing can achieve, not as proof that our specific framework has itself been built and tested. Accelerated therapeutic antiviral drug discovery pipelines are being a critical need due to viral outbreaks and rapid emergence of mutated viruses. Host Directed Therapeutics (HDTs) are new drug discovery strategies that can modulate specific host pathways essential for viral multiplication. The AI-HDT Framework is proposed to bridge the gap between the computational chemical prediction and clinical real-life application. The integration of multi-omics data such as phosphoproteomic data and transcriptomic data using the GNN models will help scientist to identify uniquely expressed host genes during the various episodes of viral infection.
How recent advances in machine learning are reshaping AMP research is examined, driving a shift from large-scale discovery toward precision-guided prediction and design and emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.
