I am working as a senior research associate (SRA) at Bioinformatics center, CSIR-Institute of Microbial Technology, Chandigarh. Currently I am working on analysis of genome wide host-virus interactions and anti HIV drug design.

Earlier, I worked as a PhD student at  IMTECH during the period 2010-2015. My topic was "Development of bioinformatics tools for antiviral molecules". During this period I worked on several projects that dealt with archival, prediction and analysis of highly potent antivirals  including small interfering RNA (siRNA), short hairpin RNA (shRNA), micro RNA (miRNA), peptides and small molecules. I developed  prediction servers, databases and knowledge bases both as a first author as well as in team work. My first assignment was to develop a resource on antiviral siRNA. In 2012, we published this resource namely, VIRsiRNAdb in Nucleic Acids Research. The online web server provides comprehensive details of 1358 siRNA/shRNA against 42 important viruses targeting indispensable viral genome regions. VIRsiRNAdb also provides the users with facilities of advance search, browsing, data submission, linking to external databases and useful analysis tools. Simultaneously, I was also working to develop an algorithm, VIRsiRNApred, for predicting inhibition efficacy of siRNAs targeting human viruses as general mammalian siRNA predictors were performing poorly on viral siRNAs. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for VIRsiRNApred model development. The same was was published in Journal of Translational Medicine (2013). 

 Following that, I proceeded on my next target, i.e., antiviral peptides (AVPs). We first focused on HIV inhibiting peptides and published a manually curated resource, HIPdb in PLoS One (2013). HIPdb harbors wide-ranging information on experimentally verified anti HIV peptides that block various steps or proteins involved in the life cycle of HIV e.g. fusion, integration, reverse transcription etc. Important fields included in  HIPdb  were peptide sequence, length, source, target, cell line, inhibition/IC50, assay and reference. The database provides user friendly sort and filter options. It also contains useful services like BLAST and ‘Map’ for alignment with user provided sequences. In addition, 3D-structure and physicochemical properties of the peptides are also included. In another study, we designed a prediction engine to select highly effective antiviral peptides targeting important human viruses like influenza, HIV, HCV, SARS, etc. We exploited various peptides sequence features such as motifs, alignment followed by amino acid composition and physicochemical properties using support vector machine. An online web server, AVPpred, was also created to help researchers working on peptide-based antiviral therapeutics and the same was published in the web server issue of Nucleic Acids Research. We also developed AVPdb, available online at, to provide a dedicated resource of experimentally verified 3307 AVPs (including 624 modified peptides) targeting over 60 medically important viruses including Influenza, HCV, HSV, RSV, HBV, DENV, SARS, etc. AVPdb was also published in Nucleic Acids Research (2014). We further designed a regression-based algorithm, AVP-IC50Pred, to predict the peptide antiviral activity in terms of IC50 values using sixteen types of sequence features and four machine learning techniques. The algorithm is available online and has other helpful services like  'AVP-IC50 Conserve' to check the conservation of user provided peptide sequence in human, viral, and antiviral proteins and  'AVP-IC50 Mutation Analyzer' to generate all possible combinations of amino acid mutations in a given peptide sequence and predict the IC50 of the mutant peptides.  AVP-IC50Pred was published in Biopolymers (formerly Peptide Science) in 2015.

 Working on another class of antivirals, namely comounds and small molecules, we developed AVCpred -an integrated web server for prediction and design of antiviral compounds. In AVCpred we used Quantitative structure-activity relationships (QSAR) approach in which relationships connecting molecular descriptors and inhibition are used to predict the antiviral potential of chemical compounds. It was published in Chemical Biology and Drug Design. We also developed a resource on viral miRNAs, namely VIRmiRNA which provides information on viral miRNAs, their targets and antiviral miRNAs in an interactive and user-friendly manner. It was published in Database (Oxford). A similar resource on chemically modified siRNAs, namely siRNAmod was published in Scientific reports (Nature). 


Area of Interest

Bioinformatics, Computational Biology, Cheminformatics, Statistics, Virology 

top publication


Qureshi A., G. Kaur , K. Manoj (2016). " AVCpred: An integrated web server for prediction and design of antiviral compounds."  Chemical Biology & Drug Design doi:10.1111/cbdd.12834 (Impact factor 2.80)]

Dar S.A., A. Thakur, A. Qureshi, K. Manoj (2016). "siRNAmod: A database of experimentally validated chemically modified siRNAs." Scientific Reports (Nature) 6, 20031; doi: 10.1038/srep20031. [Impact factor 5.57] 


Qureshi A., H. Tandon, K. Manoj (2015). "AVP-IC50 Pred: Multiple machine learning techniques based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)." Biopolymers 104(6):753-63; doi: 10.1002/bip.22703. [Impact factor 2.38]


Qureshi A., N. Thakur, I. Monga , A. Thakur, K. Manoj (2014). "VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets". Database (Oxford). doi: 10.1093/database/bau103. [Impact factor 4.45]

Qureshi A., N. Thakur, K. Manoj (2014). "AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses." Nucleic Acids Res 42(1): D1147-1153. [Impact factor 8.80]


Qureshi A., N. Thakur, K. Manoj (2013). "VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses." J Transl Med 11: 305. [Impact factor 3.99]

Qureshi A., N. Thakur, K. Manoj (2013). "HIPdb: a database of experimentally validated HIV inhibiting peptides." PLoS One 8(1): e54908. [Impact factor 3.53]


Thakur N., A. Qureshi, K. Manoj (2012). "AVPpred: collection and prediction of highly effective antiviral peptides." Nucleic Acids Res 40(Web Server issue): W199-204. [Impact factor 8.80]

Thakur N., A. Qureshi, K. Manoj (2012). "VIRsiRNAdb: a curated database of experimentally validated viral siRNA/shRNA." Nucleic Acids Res 40(Database issue): D230-236. [Impact factor 8.80]