Medicament module pharmacokinetic property and toxicity predicting method based on capsule network

Abstract

The invention provides a medicament module pharmacokinetic property and toxicity predicting method based on a capsule network. After a comprehensive module fingerprint and a module descriptor are constructed and early-period preparing operation for establishing model is performed, a low-grade characteristic content of a molecule is extracted from an upper-layer low-grade characterized through convolutional or restricted Boltzmann machine operation; then a capsule network method is used for abstracting the high-grade characteristic of the molecule in a lower-layer high-grade characteristic; a relation between the high-grade characteristic and an active label is fit through a dynamic routing algorithm, thereby predicting the pharmacokinetic property and the toxicity class of an unknown smallmolecule. The method does not require collection of large scale datasets for training, optimization is performed on input through end-to-end and furthermore automatic dimension reduction is realized.A coupling coefficient is updated through iterating a dynamic routing process. The dynamic routing conveys all characteristics of an upper-layer capsule to a random lower-layer capsule, thereby greatly reserving a hierarchical position relation. The method realizes a better predicting effect than that of a traditional machine learning method.

Description

technical field [0001] The invention relates to the field of computer-aided drug molecule design, in particular to a method for predicting pharmacokinetic properties and toxicity of drug molecules based on a capsule network. Background technique [0002] The great success of a drug depends not only on its good efficacy, but also on its excellent pharmacokinetic properties and low toxicity. According to statistics, the poor absorption, distribution, metabolism, excretion and toxicity of candidate drugs account for more than 50% of the reasons for the failure of drug development. Therefore, it is possible to exclude and optimize compounds with poor pharmacokinetic properties and toxicity in the early stage of drug development Greatly improve the success rate of drug development. In recent years, although in vitro high-throughput screening methods can be used to measure the pharmacokinetic properties and toxicity of compounds, experimental-based assays are not only expensive a...

AI Technical Summary

Problems solved by technology

This method is based on the molecular fingerprints and molecular descriptors of ligands, and uses deep learning capsule networks to establish the relationship between molecular fingerprints and molecular descriptors, pharmacokinetic properties and toxicity, and overcomes the inadequacy of predicting classification effects in the prior art. Good, the original information of molecular fingerprints and molecular descriptors that characterize ligands is seriously lost, and the accuracy of prediction is greatly dependent on the size of the training set.

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