# US 11,309,063 B2 · Identifying chemical substructures associated with adverse drug reactions

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 11,309,063 B2
- Document ID: US11309063B2
- Status: Granted
- Kind: B2
- Application number: 15/814,451
- Filed: 2017-11-16
- Published: 2018-10-25
- Granted: 2022-04-19
- Earliest claimed: 2017-04-21
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Sanjoy Dey; Achille Belly Fokoue-Nkoutche; Jianying Hu; Heng Luo; Ping Zhang
- Web page: https://patentgrep.com/patent/US11309063B2

## CPC

- G16C 20/70: Machine learning, data mining or chemometrics
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G16C 20/30: Prediction of properties of chemical compounds, compositions or mixtures
- G06N 3/04: Architecture, e.g. interconnection topology
- G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## 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.

## Claims

### Claim 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.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 2)

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

### Claim 4 (depends on 3)

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

### Claim 5 (depends on 3)

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