Training method and device for neural network for determining molecular retrosynthetic route

Abstract

The present application provides a neural network training method and device for determining molecular retrosynthetic routes. The method includes: for multiple molecules, when determining the retrosynthetic route of each molecule, the concept of layered learning is adopted to disassemble the training process of the molecular retrosynthetic route that needs to explore a large depth into multiple layers for training, and multi-layer The molecular retrosynthetic route replaces the complete retrosynthetic reaction process. After completing the training of a layer of molecular retrosynthetic route, the molecular screening method is used to select a representative molecule as the starting molecule of the next layer of molecular retrosynthetic route, which can effectively Improve the exploration efficiency of molecular retrosynthetic routes, and then more efficiently extract accurate molecular cost value information. By layering, it greatly reduces the large amount of calculation overhead caused by determining the molecular retrosynthetic route, and reduces the time for determining the molecular retrosynthetic route on the basis of ensuring the accuracy of the molecular retrosynthetic route.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a neural network training method, device, equipment and readable storage medium for determining molecular retrosynthetic routes. Background technique [0002] In recent years, with the rapid development of artificial intelligence technology, it has been gradually introduced into various scientific fields and plays an important role. In the field of chemistry, due to the infinite variety of chemical reactions under different conditions, in the past, when preparing compound molecules, researchers needed a lot of time and energy to design a reasonable organic synthesis route. By designing organic synthesis routes, researchers can greatly improve the efficiency of researchers in the development of chemical drug molecules and other compounds. [0003] At present, the methods for molecular retrosynthetic route design based on artificial intelligence include t...

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Problems solved by technology

[0004] However, it takes a long time to design molecular retrosynthetic routes using the above methods, and the above methods need to determine the maximum exploration height at the initial stage of building the molecular retrosynthesis tree. It is difficult for complex molecules to complete the construction of molecular retrosynthetic trees within a limited height; on the contrary, if the maximum exploration height is too large, the time required will increase exponentially, resulting in lower efficiency and accuracy of molecular retrosynthetic route design. Low

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