Biological data processing method and device, computer equipment and storage medium

Abstract

The invention discloses a biological data processing method and device, computer equipment and a storage medium. The method comprises the steps that atom attribute information of first atoms contained in a first molecular substance is obtained, and atom attribute information of second atoms contained in a second molecular substance is obtained; inputting the atomic attribute information of the first atom and the atomic attribute information of the second atom into a machine translation model; in the machine translation model, generating a first reaction intensity value between the first atom and each atom in the atom set and a second reaction intensity value between the second atom and each atom; the plurality of atoms in the atom set comprise a first atom and a second atom; and predicting the substance affinity between the first molecular substance and the second molecular substance according to a first reaction intensity value between the first atom and each atom and a second reaction intensity value between the second atom and each atom. By adopting the method and the device, the accuracy of the obtained material affinity can be improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular, to a biological data processing method, apparatus, computer equipment, and storage medium. Background technique [0002] With the continuous development of computer networks, machine learning has been applied in various scenarios of life. For example, the application of machine learning in the medical field. [0003] In the prior art, when predicting the affinity between two macromolecular substances through machine learning, a set of molecular feature learning rules can usually be preset, so that the model can predict the affinity between different molecular substances according to the set of rules. feature to learn. Furthermore, when the model is applied, the relevant information of two macromolecular substances can be input into the model, and the model can predict the substance affinity between the two macromolecular substances. [0004] However, the mo...

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Problems solved by technology

[0004] However, the model trained in this way usually integrates and predicts the overall characteristics of macromolecular substances. Since macromolecular substances contain complex molecular structures composed of several atoms, macromolecular substances usually contain many Complex dispersion features, which are not easy to integrate, therefore, the affinity between macromolecular substances predicted by the model trained by the existing technology is not accurate

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