| In silico approaches for designing highly effective cell penetrating peptides A Gautam, K Chaudhary, R Kumar, A Sharma, P Kapoor, A Tyagi, ... Journal of Translational Medicine 11 (74), 2013 | 270 | 2013 |
| dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides A Sharma, P Gupta, R Kumar, A Bhardwaj Scientific Reports 6, 2016 | 112 | 2016 |
| Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases A Sharma, S Rani, D Gupta International journal of biomedical imaging 2020, 1-10, 2020 | 109 | 2020 |
| Designing of peptides with desired half-life in intestine-like environment A Sharma, S Deepak, R Mamoon, RG Pal BMC Bioinformatics 15, 282, 2014 | 93 | 2014 |
| Computational approach for designing tumor homing peptides A Sharma, P Kapoor, A Gautam, K Chaudhary, R Kumar, JS Chauhan, ... Sci. Rep. 3 (1607), 2013 | 82 | 2013 |
| BIAdb: A curated database of benzylisoquinoline alkaloids D Singla, A Sharma, J Kaur, B Panwar, G Raghava BMC pharmacology 10 (1), 4, 2010 | 56 | 2010 |
| BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts A Sharma, P Dutta, M Sharma, NK Rajput, B Dodiya, JJ Georrge, T Kholia, ... Journal of cheminformatics 6, 1-10, 2014 | 51 | 2014 |
| Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis R Vashisht, AK Mondal, A Jain, A Shah, P Vishnoi, P Priyadarshini, ... Plos one 7 (7), e39808, 2012 | 47 | 2012 |
| HIVsirDB: A Database of HIV Inhibiting siRNAs A Tyagi, F Ahmed, N Thakur, A Sharma, GPS Raghava, M Kumar PloS one 6 (10), e25917, 2011 | 47 | 2011 |
| Hmrbase: a database of hormones and their receptors M Rashid, D Singla, A Sharma, M Kumar, G Raghava BMC genomics 10 (1), 307, 2009 | 30 | 2009 |
| Predicting risk of low birth weight offspring from maternal features and blood polycyclic aromatic hydrocarbon concentration SN Kumar, P Saxena, R Patel, A Sharma, D Pradhan, H Singh, R Deval, ... Reproductive Toxicology 94, 92-100, 2020 | 28 | 2020 |
| Machine learning assisted prediction of prognostic biomarkers associated with COVID-19, using clinical and proteomics data R Sardar, A Sharma, D Gupta Frontiers in Genetics 12, 522, 2021 | 25 | 2021 |
| Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis AS Gaur, A Bhardwaj, A Sharma, L John, MR Vivek, N Tripathi, ... Journal of Chemical Sciences 129, 515-531, 2017 | 20 | 2017 |
| PolysacDB: a database of microbial polysaccharide antigens and their antibodies A Aithal, A Sharma, S Joshi, GPS Raghava, GC Varshney PLoS One 7 (4), e34613, 2012 | 17 | 2012 |
| Developing a hybrid antimicrobial resistance surveillance system in India: Needs & challenges J Kaur, A Sharma, AS Dhama, H Buttolia, VC Ohri, K Walia, AK Sharma, ... Indian J Med Res 149, 299-302, 2019 | 11 | 2019 |
| SLTChemDB: A database of chemical compounds present in Smokeless tobacco products J Kaur, A Sharma, A Kumar, D Bhartiya, DN Sinha, S Kumari, R Gupta, ... Scientific Reports 9 (1), 7142, 2019 | 10 | 2019 |
| iRSVPred: a web server for artificial intelligence based prediction of major basmati paddy seed varieties A Sharma, D Satish, S Sharma, D Gupta Frontiers in Plant Science 10, 510220, 2020 | 8 | 2020 |
| Indian major basmati paddy seed varieties images dataset A Sharma, D Satish, S Sharma, D Gupta Data in Brief 33, 106460, 2020 | 2 | 2020 |
| Development of comprehensive data repository on chemicals present in smokeless tobacco products: Opportunities & challenges J Kaur, A Sharma, R Gupta, H Singh Indian Journal of Medical Research 148 (1), 4-6, 2018 | 1 | 2018 |
| Machine learning-assisted prediction of trabeculectomy outcomes among patients of juvenile glaucoma by using 5-year follow-up data S Birla, T Varshney, A Singh, A Sharma, A Panigrahi, S Gupta, D Gupta, ... Indian Journal of Ophthalmology, 10.4103, 2024 | | 2024 |
