Drug molecule screening method and system

Abstract

The invention discloses a drug molecule screening method and system. The method comprises the steps of: collecting drug molecule data related to a specific disease, carrying out the preprocessing of the data, and calculating an encoding vector and drug physicochemical properties; constructing and training an AI model based on a conditional variation auto-encoder, combining the encoding vector andthe molecular drug physicochemical property to serve as an input layer of the model, converting the encoding vector into a hidden layer encoding vector through an encoding layer of the model, generating a possible drug molecular structure through a decoding layer of the model, and in the model training process, minimizing a loss function of the model through a gradient descent algorithm, and continuously updating and iterating weight parameters of neural network structures of the encoding layer and the decoding layer; and generating potential drug molecules for curing the specific disease according to the trained model of the conditional variation auto-encoder. According to the drug molecule screening method and system, the drug physicochemical property data of the compound molecules are also utilized, the drug physicochemical property has great correlation with whether the compound can finally form a drug or not, and the druggability of the compound is improved.

Description

technical field [0001] The invention relates to a screening method, in particular to a drug molecule screening method and system. Background technique [0002] In the field of drug research and development, the traditional method is computer simulation to screen and then synthesize drugs. With the rapid development of AI medicine, people have begun to try to apply various AI algorithm models in the field of drug research and development to solve the problem of long cycle times for new drug research and development. Currently, The target information of many diseases is unknown, which makes it extremely difficult and costly to find effective drug molecules from numerous compound libraries. However, the fast computing power and innovative theoretical basis of AI bring great benefits to the screening process of drug molecules. Here comes a new way of research. For example, against the attempted application of generative networks, convolutional neural networks, recurrent neural ...

AI Technical Summary

Problems solved by technology

This makes the possible drug molecules generated have little breakthrough in the existing field, or because of the low effectiveness of their drug properties, it is difficult to enter the stage of real drug trials in terms of druggability

In addition, the existing AI models seldom take into account the properties of drug molecules, and the input layer data of the model is relatively single, which reduces the effectiveness of the drug molecules generated by the model

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