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Novel molecules\nwere designed from the output of this study.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods:<\/jats:title>\n<jats:p>A total of 3 alignment methods were used to generate CoMFA and CoMSIA models. A\nDistill based alignment method was considered a better method according to different validation parameters.\nA 3D-QSAR model was generated and contour maps were discussed. The biological activity\nof designed molecules was predicted using the generated QSAR model to validate QSAR.\nThe newly designed molecules were docked to predict binding affinity.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results:<\/jats:title>\n<jats:p>In CoMFA, leave one out cross-validated coefficient (q2), conventional coefficient (r2) and\npredicted correlation coefficient (r2\nPredicted) values were found to be 0.721, 0.991 and 0.780, respectively.\nThe best obtained CoMSIA model also showed significant cross-validated coefficient (q2),\nconventional coefficient (r2) and predicted correlation coefficient (r2\nPredicted) values of 0.714, 0.987\nand 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic\nand hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models\nwere used to design new and bioactive protease inhibitors by incorporating bioisosterism and\nknowledge-based structure-activity relationship.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Conclusion:<\/jats:title>\n<jats:p>The results from both these approaches, ligand-based drug design and structure-based\ndrug design, are adequate and promising to discover protease inhibitors.<\/jats:p>\n<\/jats:sec>","DOI":"10.2174\/1570162x18666201119143457","type":"journal-article","created":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T04:19:17Z","timestamp":1605845957000},"page":"154-171","update-policy":"https:\/\/doi.org\/10.2174\/bsp_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improved 3D-QSAR Prediction by Multiple Conformational Alignments and Molecular Docking Studies to Design and Discover HIV-I Protease Inhibitors"],"prefix":"10.2174","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4633-7229","authenticated-orcid":true,"given":"Paresh K.","family":"Patel","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad 382 481, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-0695","authenticated-orcid":true,"given":"Hardik G.","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad 382 481, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"965","reference":[{"key":"ref=1","unstructured":"UNAIDs: AIDSinfo"},{"key":"ref=2","doi-asserted-by":"publisher","first-page":"19743","DOI":"10.7448\/IAS.17.4.19743","volume":"17","author":"Pironti A.","year":"2014","unstructured":"Pironti A.; Pfeifer N.; Kaiser R.; Walter H.; Lengauer T.; Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences. 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