{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T02:54:59Z","timestamp":1791600899740,"version":"4.3.4"},"reference-count":78,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https:\/\/web.iitm.ac.in\/bioinfo2\/RSAPred\/.<\/jats:p>","DOI":"10.1093\/bib\/bbae002","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T16:49:50Z","timestamp":1706028590000},"source":"Crossref","is-referenced-by-count":54,"title":["Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5404-3266","authenticated-orcid":false,"given":"Sowmya R","family":"Krishnan","sequence":"first","affiliation":[{"name":"Department of Biotechnology , Bhupat and Jyoti Mehta School of Biosciences, , Chennai 600036 , India"},{"name":"Indian Institute of Technology Madras , Bhupat and Jyoti Mehta School of Biosciences, , Chennai 600036 , India"},{"name":"TCS Research (Life Sciences division), Tata Consultancy Services , Hyderabad 500081 , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arijit","family":"Roy","sequence":"additional","affiliation":[{"name":"TCS Research (Life Sciences division), Tata Consultancy Services , Hyderabad 500081 , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1776-4096","authenticated-orcid":false,"given":"M Michael","family":"Gromiha","sequence":"additional","affiliation":[{"name":"Department of Biotechnology , Bhupat and Jyoti Mehta School of Biosciences, , Chennai 600036 , India"},{"name":"Indian Institute of Technology Madras , Bhupat and Jyoti Mehta School of Biosciences, , Chennai 600036 , India"},{"name":"International Research Frontiers Initiative , School of Computing, , Yokohama 226-8501 , Japan"},{"name":"Tokyo Institute of Technology , School of Computing, , Yokohama 226-8501 , Japan"},{"name":"Department of Computer Science, National University of Singapore , 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