Drug small molecule activity prediction method based on bidirectional long-short memory model

Abstract

The invention discloses a drug small molecule activity prediction method based on a bidirectional long-short memory model. The method comprises the steps of: obtaining a data set; preprocessing the data set: expressing all compound molecules in a data set by using SMILES, standardizing SMILES expressions of all the molecules, unifying encoding modes and sequences of atoms, bonds and connection relationships in the SMILES expressions of the molecules, and carrying out deduplication processing by using InChIKey of the molecules; encoding the pre-processed data set, in which a single element, a single number, a single symbol and the entire square bracket of the SMILES sequence are regarded as a sequence token by one-hot encoding, each token itself has chemical significance and directivity, and the combination of any tokens complies with chemical rules; constructing a bidirectional long-short memory core fragment identification model; inputting encoded data into the bidirectional long-short memory core fragment identification model to obtain a hidden state moment; and evaluating the bidirectional long-short memory core fragment identification model.

Description

Technical field [0001] The invention relates to the fields of chemoinformatics and bioinformatics. Specifically, the present invention relates to a method and system for predicting the activity of small drug molecules based on a two-way long and short memory model. Background technique [0002] Clarifying the relationship between molecular structure and biological activity has always been an important topic in the field of medicinal chemistry. However, with the explosive growth of experimental data, it is increasingly difficult to clarify this relationship based on empirical measurement and heuristic rules. [0003] Chemoinformatics is an active research field that uses high-performance computers and machine learning methods to predict biological activity from molecular structures. In recent decades, with the emergence of deep learning methods, machine learning has attracted more and more attention from the scientific community. Data-driven analysis has become a routine procedur...

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However, no studies have directly fed SMILES into sequen

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