MODELLING FRAMEWORK FOR EMBEDDING-BASED PREDICTIONS FOR COMPOUND-VIRAL PROTEIN ACTIVITY
A global effort is underway to identify compounds to treat emerging virus infections, such as COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. The present invention discloses a machine learning representation framework that uses deep learning-induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model uses a consensus framework to rank approved compounds against viral proteins of interest.
1 . A method, comprising:
collecting chemical data including:
simplified molecular-input line-entry system (SMILES) representations of compounds;
viral protein amino acid sequences; and
compound-viral protein activities;
transforming bioactivities of the chemical data into pChEMBL values;
generating a plurality of regression models according to a plurality of different machine learning architectures;
generating a plurality of predicted activities for each pairing of one of the compounds and one of the viral protein amino acid sequences;
taking a consensus from a predefined number of top performing regression models selected from the plurality of regression models; and
outputting a list of the compounds according to the consensus having a highest predicted activity against the viral protein amino acid sequences.
2 . The method of claim 1 , wherein the SMILES representations of compounds are collected from MOSES and ChEMBL databases.
3 . The method according to claim 1 , wherein the viral protein amino acid sequences are collected from a Uniprot database.
4 . The method according to claim 1 , wherein the compound-viral protein activities are collected from NCBI, PubChem, and ChEMBL databases.
5 . The method according to claim 1 , wherein the plurality of different machine learning architectures include a Generalized Linear Model (GLM), Random Forests (RF), XGBoost, Support Vector Machines (SVM), Convolutional neural network (CNN), Long Short Term Memory (LSTM), CNN-LSTM, and Graph Attention Network (GAT)-CNN.
6 . The method according to claim 1 , wherein the top performing regression models are determined from the plurality of regression models based on performance with respect evaluation metrics including at least one of:
a mean absolute error;
a mean squared error;
a Pearson correlation R; and
a coefficient of determination.
7 . The method of claim 1 , wherein the predefined number of top performing regression models is five.
8 . The method of claim 1 , further comprising:
selecting a certain compound from the list having a lowest binding energy for at least one of the viral protein amino acid sequences examined; and
administering a therapeutically effective dose of the certain compound to a human to treat a virus associated with the viral protein amino acid sequences examined.
9 . The method of claim 8 , wherein the virus is SARS-COV-2 and the certain compound is Rifabutin.
10 . A method of treating SARS-COV-2 in a human, comprising:
administering a therapeutically effective dose of Rifabutin.
11 . The method of claim 10 , wherein the therapeutically effective dose of Rifabutin is selected from a plurality of compounds for administration to the human from a plurality of candidate compounds at a plurality of dosages that includes Rifabutin at the therapeutically effective dose, wherein a consensus framework of a plurality of machine learning models evaluated each candidate compound of the plurality of compounds against spike proteins of SARS-COV-2, wherein Rifabutin at the therapeutically effective dose exhibits a highest compound-viral protein activity against the spike proteins from the plurality of candidate compounds according to the consensus framework.
12 . A method of treating a virus in a human, comprising:
identifying viral protein amino acid sequences associated with the virus;
collecting chemical data including: simplified molecular-input line-entry system (SMILES) representations of a plurality of compounds and viral protein representations for the viral protein amino acid sequences; and
estimating compound-viral protein activities between each compound of the plurality of compounds and the viral protein amino acid sequences according to a plurality of machine learning models based on a regression task taking the SMILES representations and viral protein representations;
identifying a certain compound of the plurality of compounds having an estimated compound-viral protein activity against the viral protein amino acid sequences according to a consensus framework of the plurality of machine learning models that is above a threshold; and
administering a therapeutically effective dose of the certain compound to the human.
13 . The method of claim 12 , wherein the virus is SARS-COV-2 and the certain compound is Rifabutin.
14 . The method of claim 12 , wherein the simplified molecular-input line-entry system (SMILES) representations of a plurality of compounds and viral protein representations for the viral protein amino acid sequences are two-dimensional models.
15 . The method of claim 12 , wherein the plurality of machine learning models are developed via a corresponding plurality of different machine learning architectures including:
a Generalized Linear Model (GLM);
Random Forests (RF), XGBoost;
Support Vector Machines (SVM);
Convolutional neural network (CNN);
Long Short Term Memory (LSTM);
CNN-LSTM; and
Graph Attention Network (GAT)-CNN.