چكيده به لاتين
Solubility is one of the important parameters for achieving the desired concentration of circulating drug to achieve the required drug response. Water-soluble drugs often require high doses to achieve therapeutic plasma concentrations after oral administration. Low solubility in water is the most important issue facing the development of new chemical formulations as well as general development. Any drug that is absorbed must be present as an aqueous solution at the site of absorption. For this purpose, in this project, we have predicted the solubility of drugs. Therefore, four machine learning algorithms (linear regression, ridge regression, regression support vector, and random forest) and to teach algorithms, 199 drugs in different categories of pteridines, barbiturates, etc. have been used. In the following, several quantum properties of these drugs have been performed by quantum calculations and the models have been given to predicting the solubility of drugs using these properties, then the amount of solubility obtained with the experimental value of the solubility of 199 drugs has been investigated and the performance of 4 models It was found that the best performance was related to random forest and support vector machine. Using these algorithms, we were able to predict the solubility of drugs to an acceptable level and show how effective quantum committees would be in estimating solubility if structural committees were replaced. To improve the models in the future, we intend to add as many drugs with more diverse structures to the data as possible and also to investigate the effect of thermodynamic properties on the solubility of these drugs.
