Identifying chemical substructures associated with adverse drug reactions

Patent metadata

Field	Value
Publication number	US-11309063-B2
Application number	US-201715814451-A
Country	US
Kind code	B2
Filing date	Nov 16, 2017
Priority date	Apr 21, 2017
Publication date	Apr 19, 2022
Grant date	Apr 19, 2022

Abstract

Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.

First claim

What is claimed is:

1. A computer-implemented method for generating a framework for analyzing adverse drug reactions comprising:
receiving, to a processor, a plurality of drug chemical structures;
receiving, to the processor, a plurality of known drug-adverse drug reaction associations;
constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations, wherein constructing the deep learning framework comprises defining a plurality of neighborhood-based fingerprints for each of the plurality of drug chemical structures using a plurality of hidden layers and generating a convolutional feature map comprising a plurality of convolutional steps, wherein each convolutional step encodes a respective neighborhood-based fingerprint at a respective hidden layer, wherein each fingerprint is obtained by starting from multiple atoms belonging to said fingerprint and resultant redundancies are removed by mapping each fingerprint into a lower dimension using a single layer of the deep learning framework;
analyzing the deep learning frameworks to determine a set of substructure-adverse drug reaction associations;
identifying a plurality of top predictive fingerprints for the set of substructure-adverse drug reaction associations based upon learned weights from a final layer of the deep learning framework;
for each of the plurality of top predictive fingerprints, investigating each layer of the deep learning framework to identify atoms having a highest activation for the respective fingerprint;
reconstructing a set of substructures by starting from each identified atom and expanding the neighborhood up to the respective layer, wherein reconstructing from a first identified atom on a first layer of the deep learning framework results in expanding the respective neighborhood up to the first layer, and wherein reconstructing from a second identified atom on a second layer of the deep learning framework results in expanding the respective neighborhood up to the second layer;
calculating, for each substructure, a p value using a chi-squared test to evaluate a relative association strength between the substructure and the respective adverse drug reaction association; and
redesigning a candidate substructure of a candidate drug to avoid a determined substructure-adverse drug reaction association.

2. The computer-implemented method of claim 1 further comprising ranking substructure-adverse drug reaction associations.

3. The computer-implemented method of claim 2 further comprising grouping substructures and related adverse drug reactions with biclustering.

4. The computer-implemented method of claim 3 further comprising outputting a predicted drug-adverse drug reaction association.

5. The computer-implemented method of claim 3 further comprising outputting a substructure-adverse drug reaction map.

Assignees

• IBM

Inventors

• Dey Sanjoy
• Fokoue-Nkoutche Achille Belly
• Hu Jianying
• Luo Heng
• Zhang Ping

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/09

  Supervised learning · CPC title

• G16C20/70 Primary

  Machine learning, data mining or chemometrics · CPC title

• G16C20/30

  Prediction of properties of chemical compounds, compositions or mixtures · CPC title