ArticleRNA biology2016
SMEpred workbench: A web server for predicting efficacy of chemicallymodified siRNAs.
Article in RNA biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- CMsiRNAdb: a database of chemically modified SiRNA silencing efficiency for nucleic acid drug design.BMC bioinformatics · 2026Article
- An siRMSD parameter of structural distortion induced by chemical modification is predictive of the off-target effect of siRNA.Molecular therapy. Nucleic acids · 2025Article
- Two-Stage Probability-Enhanced Regression on Property Matrices and LLM Embeddings Enables State-of-the-Art Prediction of Gene Knockdown by Modified siRNAs.International journal of molecular sciences · 2025Article
- Small Interfering RNA Carriers for Oncotherapy: A Preclinical Overview.Pharmaceutics · 2025Review
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.Frontiers in pharmacology · 2025Article
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Artificial intelligence for drug discovery: Resources, methods, and applications.Molecular therapy. Nucleic acids · 2023Review
- Potential of siRNA in COVID-19 therapy: Emphasis onFrontiers in bioengineering and biotechnology · 2023Review
- Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs.Molecules (Basel, Switzerland) · 2022Article
- Systematics for types and effects of RNA variations.RNA biology · 2021Article
- Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs.Nucleic acids research · 2018Article
- ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy.G3 (Bethesda, Md.) · 2017Article
- An improved method for identification of small non-coding RNAs in bacteria using support vector machine.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical modifications have been extensively exploited to circumvent shortcomings in therapeutic applications of small interfering RNAs (siRNAs). However, experimental designing and testing of these siRNAs or chemically modified siRNAs (cm-siRNAs) involves enormous resources. Therefore, in-silico intervention in designing cm-siRNAs would be of utmost importance. We developed SMEpred workbench to predict the efficacy of normal siRNAs as well as cm-siRNAs using 3031 heterogeneous cm-siRNA sequences from siRNAmod database. These include 30 frequently used chemical modifications on different positions of either siRNA strand. Support Vector Machine (SVM) was employed to develop predictive models utilizing various sequence features namely mono-, di-nucleotide composition, binary pattern and their hybrids. We achieved highest Pearson Correlation Coefficient (PCC) of 0.80 during 10-fold cross validation and similar PCC value in independent validation. We have provided the algorithm in the 'SMEpred' pipeline to predict the normal siRNAs from the gene or mRNA sequence. For multiple modifications, we have assembled 'MultiModGen' module to design multiple modifications and further process them to evaluate their predicted efficacies. SMEpred webserver will be useful to scientific community engaged in use of RNAi-based technology as well as for therapeutic development. Web server is available for public use at following URL address: http://bioinfo.imtech.res.in/manojk/smepred .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.