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 ...

Examples

Embodiment Construction

[0029] Such as figure 1 As shown, the drug molecular screening method of an embodiment of the present invention includes:

[0030] Step S101, preprocessing: collecting drug molecular data related to a specific disease, preprocessing the data, calculating its encoding vector and related drug physicochemical properties, forming structural data and storing it in the database;

[0031] Step S103, building a training model: building and training an AI model based on a conditional variational autoencoder, using the combination of the encoding vector and the drug physicochemical properties of the molecule as the input layer of the model, converting the encoding layer of the model into a hidden layer encoding vector, and then The possible drug molecular structure is generated through the decoding layer of the model. During the model training process, the model loss function is minimized through the gradient descent algorithm, and the weight parameters of the neural network structure o...

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 ...

Examples

Embodiment Construction

[0029] Such as figure 1 As shown, the drug molecular screening method of an embodiment of the present invention includes:

[0030] Step S101, preprocessing: collecting drug molecular data related to a specific disease, preprocessing the data, calculating its encoding vector and related drug physicochemical properties, forming structural data and storing it in the database;

[0031] Step S103, building a training model: building and training an AI model based on a conditional variational autoencoder, using the combination of the encoding vector and the drug physicochemical properties of the molecule as the input layer of the model, converting the encoding layer of the model into a hidden layer encoding vector, and then The possible drug molecular structure is generated through the decoding layer of the model. During the model training process, the model loss function is minimized through the gradient descent algorithm, and the weight parameters of the neural network structure o...

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