乐胖代购免代理版

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In standard VS exercise, roughly 10% of top-ranked molecules exhibit activity when examined in biochemical assays, which accounts for many false positive hits, making it an arduous task. Attempts for conquering false-hit rates were developed through either ligand-based or structure-based VS separately; however, nonetheless performed remarkably well. Here, we present an advanced VS framework\u2014automated hit identification and optimization tool (A-HIOT)\u2014comprises chemical space-driven stacked ensemble for identification and protein space-driven deep learning architectures for optimization of an array of specific hits for fixed protein receptors. A-HIOT implements numerous open-source algorithms intending to integrate chemical and protein space leading to a high-quality prediction. The optimized hits are the selective molecules which we retrieve after extreme refinement implying chemical space and protein space modules of A-HIOT. Using CXC chemokine receptor 4, we demonstrated the superior performance of A-HIOT for hit molecule identification and optimization with tenfold cross-validation accuracies of 94.8% and 81.9%, respectively. In comparison with other machine learning algorithms, A-HIOT achieved higher accuracies of 96.2% for hit identification and 89.9% for hit optimization on independent benchmark datasets for CXCR4 and 86.8% for hit identification and 90.2% for hit optimization on independent test dataset for androgen receptor (AR), thus, shows its generalizability and robustness. In conclusion, advantageous features impeded in A-HIOT is making a reliable approach for bridging the long-standing gap between ligand-based and structure-based VS in finding the optimized hits for the desired receptor. The complete resource (framework) code is available at https:\/\/gitlab.com\/neeraj-24\/A-HIOT<\/jats:ext-link>.<\/jats:p>\n Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s13321-022-00630-7","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T11:05:03Z","timestamp":1658487903000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine intelligence-driven framework for optimized hit selection in virtual screening"],"prefix":"10.1186","volume":"14","author":[{"given":"Neeraj","family":"Kumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2175-9799","authenticated-orcid":false,"given":"Vishal","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"630_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fchem.2020.00343","author":"EHB Maia","year":"2020","unstructured":"Maia EHB, Assis LC, de Oliveira TA et al (2020) Structure-based virtual screening: from classical to artificial intelligence. 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