The invention belongs to the technical field of
drug screening, and provides a multi-
target drug screening and optimizing method based on
machine learning, which comprises the following steps: S1, carrying out
feature screening on compound molecular descriptors by utilizing a
machine learning
algorithm, and determining the
molecular descriptor which has the most significant influence on
biological activity; s2, constructing a
quantitative structure-activity relation model, and predicting the
biological activity of the compound; s3, based on a multi-objective optimization
algorithm, combining absorption, distribution,
metabolism,
excretion and
toxicity properties of the compound; according to the method,
feature screening is carried out on compound molecule descriptors by utilizing a
machine learning
algorithm, a QSAR model is constructed to predict the
biological activity, ADMET property classification prediction is combined, the biological activity, ADMET properties and pharmacokinetic characteristics of drugs are comprehensively considered, and the accuracy and accuracy of
drug activity prediction are improved. The method can efficiently and accurately screen and optimize
drug candidate compounds with potential drug effects and good pharmacokinetic characteristics, so that the success rate of drug research and development is increased, and the research and development cost and time are reduced.