Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract#R##N#Small molecule aggregators non-specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high-throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries.  used 1319 aggregators and 128,325 non-aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross-validation, which shod comparable aggregator and significantly improved non-aggregator identification rates against earlier studies. The second is the independent test of 17 aggregators discovered independently from the training aggregators, 71% of which re correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non-aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1.14% of which re predicted as aggregators. SVM shod slightly better overall performance against two other machine learning methods based on five fold cross-validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM shod substantial capability in identifying aggregators from large libraries at low false-hit rates. © 2009 Wiley Periodicals, Inc.J Comput Chem, 2010
['Hanbing Rao', 'Ze-Rong Li', 'Xiang-Yuan Li', 'Xiao Hua Ma', 'Choong Yong Ung', 'Hu Li', 'Xianghui Liu', 'Yu Zong Chen']
I am writing a research paper on a idea Identification of small molecule aggregators from large compound libraries by support vector machines
[{"from": "human", "value": "I am writing a research paper on a idea Identification of small molecule aggregators from large compound libraries by support vector machines"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract#R##N#Small molecule aggregators non-specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high-throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries.  used 1319 aggregators and 128,325 non-aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross-validation, which shod comparable aggregator and significantly improved non-aggregator identification rates against earlier studies. The second is the independent test of 17 aggregators discovered independently from the training aggregators, 71% of which re correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non-aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1.14% of which re predicted as aggregators. SVM shod slightly better overall performance against two other machine learning methods based on five fold cross-validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM shod substantial capability in identifying aggregators from large libraries at low false-hit rates. \u00a9 2009 Wiley Periodicals, Inc.J Comput Chem, 2010"}]