Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show how chemical background knowledge can be used to improve the prediction performance in structureactivitity relationships (SARs) for non-congeneric compounds. The goal of the study is to build a model of growthinhibition for the NCI DTP human tumor cell line screening data. The SAR model is based on frequent molecular fragments generated from the structure data. Background knowledge in the form of standard anti-cancer agents (ACAs) grouped by known mechanisms of action is available and used twice: First, the standard agents are treated separately in the fragment generation process. Second,  represent each molecule in terms of the similarities with structures known to be associated with certain mechanisms of action. In experiments,  show that using chemical background knowledge in this way reduces the mean absolute error (MAE) by about 5 % compared to initial experiments, and by 9 % compared to a previous publication.  conjecture that specific instances and groups of instances are a commonly occurring type of background knowledge that is particularly easy to use and effective in practice.
['Lothar Richter', 'Stefan Hechtl', 'Stefan Kramer']
I am writing a research paper on a idea Leveraging Chemical Background Knowledge for the Prediction of Growth Inhibition
[{"from": "human", "value": "I am writing a research paper on a idea Leveraging Chemical Background Knowledge for the Prediction of Growth Inhibition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show how chemical background knowledge can be used to improve the prediction performance in structureactivitity relationships (SARs) for non-congeneric compounds. The goal of the study is to build a model of growthinhibition for the NCI DTP human tumor cell line screening data. The SAR model is based on frequent molecular fragments generated from the structure data. Background knowledge in the form of standard anti-cancer agents (ACAs) grouped by known mechanisms of action is available and used twice: First, the standard agents are treated separately in the fragment generation process. Second,  represent each molecule in terms of the similarities with structures known to be associated with certain mechanisms of action. In experiments,  show that using chemical background knowledge in this way reduces the mean absolute error (MAE) by about 5 % compared to initial experiments, and by 9 % compared to a previous publication.  conjecture that specific instances and groups of instances are a commonly occurring type of background knowledge that is particularly easy to use and effective in practice."}]