Method for predicting protein and ligand molecule binding free energy based on convolutional neural network

A technology of convolutional neural network and ligand molecules, which is applied in the field of predicting the binding free energy of proteins and ligand molecules based on convolutional neural networks, can solve problems such as limited application, difficult generalization of homologue ligands, and difficulties in large-scale application , to achieve the effects of small error, good generalization ability, and precise protein-ligand binding free energy calculation

Pending Publication Date: 2021-01-05
SHENZHEN JINGTAI TECH CO LTD
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Problems solved by technology

For example, the FEP method faces problems such as the preparation of the simulation system, the processing of the solvent model, the selection of the force field, and the calculation cost, which makes it difficult to apply on a large scale in practice.
The scoring function method is mainly obtained by training and fitting on some data sets with very different chemical structures, and it is difficult to generalize to homologue ligands with small structural differences, so it has limited application in the lead optimization scenario.

Method used

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  • Method for predicting protein and ligand molecule binding free energy based on convolutional neural network
  • Method for predicting protein and ligand molecule binding free energy based on convolutional neural network
  • Method for predicting protein and ligand molecule binding free energy based on convolutional neural network

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Embodiment 1

[0049] Binding free energy prediction for a kinase target:

[0050] First, collect the small molecule inhibitors of the target, perform 3D conformation preparation and molecular docking calculation, and then call the molecular descriptor module to process these molecules and proteins and input them into the convolutional neural network model. Such as image 3 As shown, all the molecular data are divided into 5 parts, one of which is selected as the verification set each time, and the other four are used as the training set, so that the model will be trained for 5 rounds in total, that is, 5-fold cross-validation, and finally all the verification scores will be taken as The average is used as the final validation score. Figure 4 , Figure 5 Indicates the performance on the verification set during model training, from Figure 4 It can be seen from the figure that the error in the initial stage of the model is relatively large, but after about 50 rounds of training, the error...

Embodiment 2

[0054] Lead compound structure optimization for a target:

[0055] Some seed compounds with good initial activity were obtained through virtual screening, and the convolutional neural network model was used to optimize the structure of this batch of seed compounds to obtain lead compounds.

[0056] Molecular docking of hit compounds and targets is first performed, and then they are encoded and molecular descriptors are calculated. Input it into the convolutional neural network model to predict the binding free energy of the compound to the target. According to the binding mode between the hit compound and the target, as well as the binding free energy value and spatial hierarchy information predicted by the model, the structure of the hit compound is modified and optimized, and groups matching the spatial hierarchy of the target are added to the hit compound , form a better structural complementarity with the target to design new compounds. Afterwards, molecular docking and ...

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Abstract

The invention provides a method for predicting protein and ligand molecule binding free energy based on a convolutional neural network. The method comprises the following steps: (1) obtaining a high-quality protein-ligand compound crystal structure from a PDB database, collecting a small molecular structure from literature, and performing 3D conformation preparation; (2) performing encoding and descriptor calculation on the protein and the small molecules, and processing the protein into a 3D image; (3) using the 3D image of the protein as an input, and designing a convolutional neural networkarchitecture which comprises an input layer, a hidden layer and an output layer; (4) carrying out feature extraction and fine tuning on the pre-trained model so as to be applied to a new data set; and (5) after all the tasks are completed, calling Panda and Matplotlib data analysis libraries, and directly drawing an analysis result curve in Jupyter. According to the method, rapid and accurate protein-ligand binding free energy calculation is achieved, compared with an experimental value, errors are small, and data analysis and visualization can be automatically conducted on the result.

Description

technical field [0001] The invention belongs to the field of drug research and development based on artificial intelligence, specifically a method for predicting the binding free energy of proteins and ligand molecules based on convolutional neural networks, and realizes the precise prediction of the binding freedom of receptors and drug molecules by applying convolutional neural networks (CNN) It can be applied to the design and development of new drugs. Background technique [0002] New drug design and development is a creative and exploratory research work. Drug molecular design is based on rational strategies and scientific planning to construct new molecular entities with expected pharmacological activities. Molecular design is to gradually optimize active compounds into compounds that are safe, effective, controllable and easy to obtain in the human body, and meet the requirements for multidimensional properties of drugs during the process of changing and modifying st...

Claims

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Application Information

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IPC IPC(8): G16B15/20G16B15/30G06N3/04G06N3/08
CPCG16B15/20G16B15/30G06N3/08G06N3/045
Inventor 王辉马健张佩宇方磊温书豪赖力鹏
Owner SHENZHEN JINGTAI TECH CO LTD
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