Prediction model for determining whether feature vector of data in each of multiple input sequences should be added to that of the other data in the sequence

Abstract

A method is provided for creating a prediction model that predicts chemical properties of a compound from sequence data as feature vectors describing the compound. The sequence data includes multiple data sequences. The method includes generating a probabilistic prediction model y* for predicting an objective variable y and learned using Bayesian criterion and variational approximation. The method includes configuring the model to (i) assign one of multiple prediction functions for each of the feature vectors extracted from the sequence data, (ii) identify a relationship between a t-th vector in an i-th data and the objective variable y, and (iii) identify similarities of relationships between the feature vectors and the objective variable y. The method includes identifying, using the model, a sequence length which is variable between the multiple data sequences. The method includes predicting the objective variable y as a chemical property of the compound based on the model.