{"title":"AIoptamer: Artificial Intelligence-Driven Aptamer Optimization Pipeline for Targeted Therapeutics in Healthcare.","authors":"Tushar Gupta, Priyanka Sharma, Sheeba Malik, Pradeep Pant","doi":"10.1021/acs.molpharmaceut.5c00343","DOIUrl":null,"url":null,"abstract":"<p><p>Aptamers are short, single-stranded DNA or RNA molecules known for their high specificity and affinity toward target biomolecules, making them powerful tools in drug discovery, diagnostics, and biosensing. However, conventional aptamer selection methods such as SELEX (Systematic Evolution of Ligands by EXponential Enrichment) are often labor-intensive, time-consuming, and resource-demanding. To overcome these limitations, we introduce a novel AI-driven aptamer optimization pipeline (AIoptamer: AI-driven optimization of aptamers) that integrates artificial intelligence with advanced classical computational approaches to accelerate aptamer discovery and design. The workflow begins with a known aptamer-host complex and systematically generates all possible aptamer sequence variants to target the same host. These variants are then screened using AI-based models that rank them based on sequence features and predicted binding affinity. Top candidates undergo structural modeling through CHIMERA_NA, an in-house mutagenesis tool designed to perform structural mutations in nucleic acids. The modeled structures are further evaluated using PredPRBA, a deep learning-based scoring function tailored for RNA-protein binding affinity prediction and PDA-Pred, a machine learning based model for predicting DNA-protein binding affinity. The highest-ranking aptamer-host complexes are then refined through molecular dynamics (MD) simulations to assess structural stability and interaction strength at the atomic level. Our pipeline demonstrates effectiveness across both RNA and DNA aptamer complexes, offering a generalized and robust framework for aptamer optimization. By combining AI-powered prediction with conventional computational techniques, our method advances the rational design of aptamers and significantly reduces reliance on traditional experimental trial-and-error strategies, making aptamer optimization faster, scalable and more efficient.</p>","PeriodicalId":52,"journal":{"name":"Molecular Pharmaceutics","volume":" ","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Pharmaceutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1021/acs.molpharmaceut.5c00343","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Aptamers are short, single-stranded DNA or RNA molecules known for their high specificity and affinity toward target biomolecules, making them powerful tools in drug discovery, diagnostics, and biosensing. However, conventional aptamer selection methods such as SELEX (Systematic Evolution of Ligands by EXponential Enrichment) are often labor-intensive, time-consuming, and resource-demanding. To overcome these limitations, we introduce a novel AI-driven aptamer optimization pipeline (AIoptamer: AI-driven optimization of aptamers) that integrates artificial intelligence with advanced classical computational approaches to accelerate aptamer discovery and design. The workflow begins with a known aptamer-host complex and systematically generates all possible aptamer sequence variants to target the same host. These variants are then screened using AI-based models that rank them based on sequence features and predicted binding affinity. Top candidates undergo structural modeling through CHIMERA_NA, an in-house mutagenesis tool designed to perform structural mutations in nucleic acids. The modeled structures are further evaluated using PredPRBA, a deep learning-based scoring function tailored for RNA-protein binding affinity prediction and PDA-Pred, a machine learning based model for predicting DNA-protein binding affinity. The highest-ranking aptamer-host complexes are then refined through molecular dynamics (MD) simulations to assess structural stability and interaction strength at the atomic level. Our pipeline demonstrates effectiveness across both RNA and DNA aptamer complexes, offering a generalized and robust framework for aptamer optimization. By combining AI-powered prediction with conventional computational techniques, our method advances the rational design of aptamers and significantly reduces reliance on traditional experimental trial-and-error strategies, making aptamer optimization faster, scalable and more efficient.
期刊介绍:
Molecular Pharmaceutics publishes the results of original research that contributes significantly to the molecular mechanistic understanding of drug delivery and drug delivery systems. The journal encourages contributions describing research at the interface of drug discovery and drug development.
Scientific areas within the scope of the journal include physical and pharmaceutical chemistry, biochemistry and biophysics, molecular and cellular biology, and polymer and materials science as they relate to drug and drug delivery system efficacy. Mechanistic Drug Delivery and Drug Targeting research on modulating activity and efficacy of a drug or drug product is within the scope of Molecular Pharmaceutics. Theoretical and experimental peer-reviewed research articles, communications, reviews, and perspectives are welcomed.