Identifying chemical substructures associated with adverse drug reactions

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.

BACKGROUND

The present invention generally relates to adverse drug reactions, and more specifically, to identifying chemical substructures associated with adverse drug reactions.

Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. ADRs represent a significant public health problem all over the world. In the United States, it is estimated that over 2 million serious ADRs occur among hospitalized patients, resulting in over 100,000 deaths each year. Moreover, ADRs are a contributing factor to the high expenditure and low effectiveness of laboratory-based pharmaceutical drug development. Readily available information in the early stages of drug development can often be limited to the chemical structure of a drug candidate. However, predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs associated with drug discovery.

Finding novel associations between chemical substructures and ADRs could guide research efforts toward drug candidates that are more likely to lead to safe and efficacious drugs. For instance, if a chemical substructure is determined to be associated with ADRs, researchers could design drug candidates that do not incorporate such substructures. Elucidating such detailed relationships among chemical substructures and ADRs could infer new knowledge for domain experts, for instance, that could be utilized to redesign a drug under development and, thus, increase investigative efforts leading to viable candidates.

SUMMARY

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. Such computer-implemented methods can enable a-priori identification of chemical substructures likely to result in ADRs.

Embodiments of the invention are directed to a computer program product for analyzing adverse drug reactions. A non-limiting example of the computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform a method. A non-limiting example of the method includes receiving a plurality of drug chemical structures. The non-limiting example also includes receiving a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing 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. Such computer program products can enable a-priori identification of chemical substructures likely to result in ADRs.

Embodiments of the invention are directed to a processing system for analyzing adverse drug reactions. A non-limiting example of the processing system includes a processor in communication with one or more types of memory. The processor can be configured to receive a plurality of drug chemical structures. The processor can also be configured to receive a plurality of known drug-adverse drug reaction associations. The processor can also be configured to construct 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. Such processing systems can enable a-priori identification of chemical substructures likely to result in ADRs.

Embodiments of the present invention are directed to a computer-implemented method for predicting chemical substructures associated with adverse drug reactions. A non-limiting example of the computer-implemented method includes generating a plurality of raw drug features. The non-limiting example also includes pooling the plurality of significant substructures into a plurality of fixed-sized vectors. The non-limiting example also includes generating a plurality of fixed-length fingerprint representations based at least in part up on the plurality of fixed-sized vectors. The non-limiting example also includes building a final predictive model based at least in part upon the fixed-length fingerprint representations. Such methods can generate accurate chemical fingerprints associated with known ADRs.

Embodiments of the present invention are directed to a system for predicting chemical substructures associated with adverse drug reactions. A non-limiting example of the system includes a drug-ADR association prediction module. A non-limiting example of the system also includes a significant association identification module. A non-limiting example of the system also includes a neighborhood substructure association module. A non-limiting example of the system also includes a grouping module. Such systems can generate accurate chemical fingerprints associated with known ADRs.

DETAILED DESCRIPTION

Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. The terms “at least one” and “one or more” are intended to include any integer number greater than or equal to one, i.e. one, two, three, four, etc. The terms “a plurality” are intended to include any integer number greater than or equal to two, i.e. two, three, four, five, etc. The term “connection” is intended to include both an indirect “connection” and a direct “connection.”

Thus, as configured inFIG. 1, the system100includes processing capability in the form of processors101, storage capability including system memory114and mass storage104, input means such as keyboard109and mouse110, and output capability including speaker111and display115. In one embodiment, a portion of system memory114and mass storage104collectively store an operating system such as the AIX® operating system from IBM Corporation to coordinate the functions of the various components shown inFIG. 1.

Turning now to an overview of technologies that are more specifically relevant to aspects of the invention, according to the World Health Organization (WHO), an ADR is generally defined as an unintended and harmful reaction suspected to be caused by a drug taken under normal conditions. Identifying potential ADRs of drug candidates in the early stage of the drug development pipeline can improve drug safety, reduce risks for patients, and reduce monetary costs to pharmaceutical companies.

Information available in the early stages of drug development can be largely limited to the chemical structure of the drug candidate. The molecular structures of drugs can be leveraged in drug development where specific chemical substructures of drugs responsible for the ADRs can be identified. Thus, some existing studies on ADR prediction have focused on analyzing the chemical properties of drug molecules. Elucidating such detailed relationships between such chemical substructures and ADRs has potential to infer new knowledge for domain experts that can be used to redesign a drug under development and, thus, increase drug efficacy while minimizing the risk to patients and monetary expenditure associated with research and development. However mechanisms of ADRs can be complicated and not well understood presenting several challenges.

In some embodiments of the invention, each drug molecule can be represented in a suitable feature vector based upon its chemical structure and machine learning can be leveraged to predict ADRs a-priori. Some embodiments of the invention and have the capability of exploring all possible chemical substructures available in a set of drugs or drug candidates.

Embodiments of the invention advantageously provide a neural fingerprint method in a simultaneous deep learning framework for ADR prediction such that label information (including drug-ADR associations) can be used in the feature generation stage of a machine learning process. Some embodiments of the invention include interpretation and analysis of generated features to evaluate their associations for the prediction of ADRs in new drugs.

Turning now to an overview of the aspects of the invention, one or more embodiments of the invention address the above-described shortcomings of the prior art by providing a methodology and system for identifying substructures of chemical compounds that have significant associations with ADRs using a machine learning approach. Embodiments of the invention can systematically identify sub structures of chemical compounds that have significant association with ADRs, which can provide actionable insights for drug design. “Significant association” as used herein means an association that is statistically significant as determined by one or more statistical methods. Embodiments of the present invention can provide additional insights concerning the underlying reasons that certain substructures can induce ADRs in addition to predicting ADRs from drugs and drug candidates.

The above-described aspects of the invention address the shortcomings of the prior art by ranking substructure-ADR pairs obtains from generated models to systematically analyze the relationships among groups of chemical substructures within groups of related ADRs using bi-clustering based machine learning techniques. Through such techniques, drug discovery efforts can include the redesign of specific parts of identified substructures of a drug in response to and/or in connection with the relationship analysis.

Turning now to a more detailed description of aspects of the present invention,FIG. 2depicts a method200of identifying chemical substructures associated with ADRs according to one or more embodiments of the present invention. As is shown, the method200includes constructing a deep learning framework for each of a plurality of ADRs based at least in part upon drug chemical structures and known drug-ADR associations as shown at block202. As used herein, “drug chemical structure” is understood to mean the complete chemical structure of a pharmaceutical drug or candidate pharmaceutical drug. Known drug-ADR associations include ADRs known to be associated with a drug through clinical testing, therapeutic administration, and the like. The method200also includes, as shown at block204, analyzing the deep learning framework to determine substructures related to each ADR and generate substructure-ADR associations. As used herein, “substructure” is intended to mean a portion of a chemical structure of a chemical compound. The method200also includes, as shown at block206, determining significant substructure-ADR associations and ranking the significant substructure-ADR associations. The method200also includes, as shown at block208, grouping substructures and related ADRs using biclustering. Optionally, the method200includes outputting predicted drug-ADR associations, significant chemical substructures, and/or global substructure-ADR mapping as shown at block210.

The deep learning framework can include a neural fingerprint based predictive model. In some embodiments of the invention, a convolutional neural fingerprint framework is generated wherein the neighborhood of each atom is explored iteratively based upon hidden layers by representing a drug in either a 2D or a 3D graph. ADR prediction can be formulated as a binary prediction problem, wherein a predictive model is built for each ADR using chemical fingerprints as features.

In some embodiments of the invention, constructing a deep learning framework for each of a plurality of ADRs based at least in part upon drug chemical structures and known drug-ADR associations. Drug chemical structures and known drug-ADR associations can be obtained automatically or manually. Public databases contain a variety of information regarding known drugs and ADRs, including chemical structural data and chemical data. These information sources can contain structured or unstructured data. Drug chemical structures and known drug-ADR associations can include structured data, unstructured data, or both structured and unstructured data. As used herein, structured data includes data that is categorized or grouped in accordance with a system of defined rules. As used herein, unstructured data includes data that is not categorized or grouped in accordance with a system of defined rules. For example, unstructured data includes, but is not limited to, data published in journal articles in a narrative format. In exemplary embodiments, known drug data includes data from databases generally known to persons of ordinary skill in the art. For example, known drug data can include data from the DrugBank database, UniProt, Unified Medical Language System TM, PubMed, and/or various scientific journals, including, but not limited to, the Journal of Clinical Oncology, JAMA, BJC, and Clinical Infectious Diseases. Adverse drug event data includes information related to adverse events associated with a drug. Adverse drug event data can include, for example, the incidence, prevalence, or severity of events such as bleeding, paralysis, hyperkalemia.

FIG. 3depicts an exemplary method300for constructing a deep learning framework for a plurality of adverse drug reactions according to one or more embodiments of the present invention. The method300includes, as shown at block302, generating raw drug features. The method300also includes, as shown at block304, generating convolutional feature maps. The method300also includes pooling substructures into fixed sized vectors, as shown at block306. The method300also includes, as shown at block308, generating fixed-length fingerprint representations. The method300also includes, as shown at block310, building a final predictive model.

Embodiments of the invention including generating raw drug features can include, for instance, representing each drug into a 2D or 3D graphical structure. After generation of the graphical structure, in some embodiments of the invention, chemical features for each constituent atom in the drug can be extracted. For example, known fingerprint algorithms, such as ECFP, can be used to derive one or more features such as atomic element, degree, numbers of attached hydrogen atoms, implicit valence, aromaticity indicator, and/or bond type. Each drug can be represented by a matrix X∈Rnxxd, where nxrepresents the number of atoms in drug X and d is the total number of features for each atom. Let xi∈Rdrepresent the feature vector of each atom i∈{1, . . . , nx}.

Some embodiments of the invention include generating convolutional feature maps. A convolutional step can, for instance, represent a substructure in a particular layer into a condensed feature vector. In each interaction, or layer, of the algorithm, each substructure in current layer l (represented by each atom i referred as center and any neighbors explored in previous layers) can be expanded to include the immediate neighbors of each atom belonging to that substructure. Subsequently, all atomic features and bonding information of the atoms included within the substructure from previous layers can be concatenated into a large feature vector of size dl−1and redefined as new feature vector xil−1∈Rdl−1.

Each substructure can be obtained by starting the search from multiple atoms belonging to substructures and, thus, can be obtained from multiple centers. To remove resultant redundancies, each substructure xil−1can be mapped into lower dimensions using a single layer of neural network with dl−1input nodes and dloutput nodes. A weight matrix of H∈Rdlxdl+1can be defined as follows: xil=f(xil−1H+b) where b∈R. In this instance, f is a smoothing function to reduce susceptibility to minor variations in substructure.

Some embodiments of the invention include pooling multiple substructures into fixed sized vectors. For example, after generation of convolutional features maps in each level, similar substructures can be pooled into a fixed-sized feature vector of size K (hyper-parameter) using another level of neural network of weights F and a softmax, for instance, which has been shown to have a concise set of fingerprint representations for larger drug molecules. A simple addition function can be used to summarize the activation score of each atom that belongs to a given molecule in the pooling stage of the convolutional neural network.

In some embodiments of the invention, steps of generating convolutional feature maps and pooling multiples substructures into fixed-sized vectors are iterated for each radius of the molecule up to a maximum radius of the substructure L using a separate hidden layer to successively explore all possible substructures up to a maximum path length of 2L−1. Thereafter, in some embodiments of the invention, fingerprint vectors obtained from each layer are pooled (or summarized) into a final representation by summing them into a final fixed-length fingerprint representation.

Embodiments of the invention include building final predictive models. In some embodiments of the invention, upon generation of a final fingerprint representation for each drug, a fully connected neural network can be used to evaluate the ability to predict an ADR. For each ADR, for example, drugs associated with the ADR can be labeled as positives and the remaining drugs can be labeled as negatives. A predictive model can be built, for instance, using L2-norm regularized logistic regression separately for each ADR using final fingerprint representations as features. A loss function can be describe according to the following formula:

wherein, Z is the matrix containing all fingerprints for each drug denoted as zi∈RW, is a maximum radius for substructures, K is the number of fingerprints with the best F−1 score, λ is a regularization parameter, and f is a non-linear function (for example rectification with rectified linear unit (ReLU)). Batch normalization can be used to optimize each batch of size 100 using a known algorithm, such as ADAM. Hyper-parameters of the algorithms such as λ, R, K, can be selected during cross-validations. Further tuning of parameters can be performed for neural fingerprints based upon F1-scores, such as the numbers of neurons in the hidden layers and in the final layer.

Embodiments of the invention can include generating substructure-ADR associations. Extraction and interpretation of the important fingerprints of drugs can provide useful information concerning ADRs. For example, known ADRs can be analyzed with the deep learning framework to by determine substructures related to each ADR by identifying the top predictive fingerprints based upon learned weights from the final layer of the neural network. For each of the top predictive fingerprints, each layer can be investigated to identify atoms and associated drug molecules having the highest activation for the fingerprint during a first convolution step. Subsequently, substructures can be reconstructed by using the identified atom as center and expanding the neighborhood up to the associated layer.

FIG. 4depicts an exemplary system400for identifying chemical substructures associated with ADRs according to one or more embodiments of the present invention. The system400includes an input including training data402, a deep learning ADR prediction hub404, and an output406. Training data can include, for instance, chemical structures of drugs as represented by neighborhood-based fingerprints and already known drug-ADR associations. In some embodiments, not illustrated inFIG. 4, the system can include optional input, such as domain knowledge about potential substructures of ADRs or existing relationships among substructures of drugs. The ADR prediction hub404can include a drug-ADR association prediction module408, a significant association identification module410, a neighborhood substructure association module412, and/or a grouping module414. The output406can include, for example, a predicted drug-ADR association416. The output406can also include significant chemical substructures for ADRs418. “Significant chemical substructures” as used herein means chemical substructures that have a statistically significant association with an ADR. The output406can also include global substructure-ADR maps420.

In some embodiments of the invention, drug-ADR association prediction module408can construct a deep learning framework for each of a plurality of ADRs based at least in part upon drug chemical structures and known drug-ADR associations.

Significant association identification module410can analyze the deep learning frameworks to determine substructures related to each ADR and generate substructure-ADR associations.

Neighborhood substructure association module412can determine significant substructure-adverse drug reaction associations, for example with a chi-squared test, and rank significant substructure-adverse drug reaction associations, for example, according to statistical significance.

Grouping module414can group substructures and related ADRs using biclustering.

FIG. 5illustrates generation of substructure-ADR associations according to exemplary embodiments of the invention. For example, a combined learning framework using deep learning networks can be employed. As is shown at500, molecular structures of drugs can be analyzed to generate fingerprint feature representations. Subsequently, as shown at502, a fully connected neural network as a predictive model can be applied to the fingerprint feature representations. A plurality of ADRs508,510,512can be output, as is illustrated at504, and associated at506with chemical substructures514,516, and518, respectively.

Embodiments of the invention include determining significant substructure-adverse drug reaction associations. For example,FIG. 6Aillustrates a confusion matrix calculated for a given substructure A regarding exemplary ADR X from a SIDER database. As is illustrated, a represents the number of drugs that contain substructure A and cause ADR X; b is the number of drugs that do not contain substructure A but trigger ADR X; c is the number of drugs that contain substructure A but have no association towards ADR X; and d is the number of drugs that do not contain substructure A and have no association towards ADR X. Some embodiments of the invention include calculating a p value using chi-squared tests and odds ratio to evaluate the association strength between substructure A and ADR X. In some embodiments of the invention, substructure-ADR associations are ranked based upon a statistical analysis.

Embodiments of the invention include grouping substructures and related ADRs using biclustering. For instance, substructures that are associated with the ADRs can be further grouped into higher levels because many of the ADRs are inherently related. For example, available ADRs can be classified into a hierarchical graph by organizing them from specific to generic categories.

For instance, to identify substructures that are responsible for a particular group of ADRs could provide an early guideline for avoiding those related substructures or their continuous spectrum of representations during drug development. Accordingly, significant substructure-ADR pairs can be grouped based upon a guilt by association principle. For example, significant substructure-ADR associations can be included in a bipartite graph, where substructures are represented in one layer and ADRs in another layer and, wherein an edge between them represents a significant association obtained from the previous step. Consequently, biclustering algorithms can be applied to find the higher level groupings (bi-cliques) of sub structure-ADR pairs.

FIG. 6Billustrates global mapping of substructures and ADRs. Statistically significant substructures can be mapped, for example graphically, to associated ADRs.

FIG. 7illustrates an exemplary combined learning framework using a deep learning network according to one or more embodiments of the invention. A plurality of drug representations including atoms and atomic properties can be obtained. These can be used to build convolutional networks to map fingerprints, as is illustrated. For example, building convolutional networks can be analogous or equivalent to hashing. Hidden layers of convolutional network can be included for each iteration (or radius). The exemplary framework can include pooling to generate final fingerprints. The final fingerprints can be used to generate a fully connected network for predicting an ADR.

Embodiments of the invention can automatically identify substructures of chemical compounds that have significant associations with ADRs using a deep learning approach, which can provide actionable insights for drug development and safety. Embodiments of the invention can rank substructure-ADR pairs obtained from deep learning models to systematically analyze the relationships among the groups of chemical substructures with groups of related ADRs using biclustering based machine learning techniques. Embodiments of the invention are useful for discovering relationships among chemical features and ADRs and can also be used for a small set of ADRs. Some embodiments of the invention can define chemical substructures without a-priori substructure definition. Embodiments of the invention can advantageously explore the statistical significance of chemical substructure-ADR pairs and their higher order groupings.

Embodiments of the invention can leverage a state-of the art convolutional deep learning network to simultaneously construct chemical fingerprints and their capabilities toward predicting ADR in a single learning framework. Such learning steps can result in a parsimonious set of fingerprints, for example, because the model can be limited to learn only those fingerprints that are predictive of ADRs, thus automatically filtering irrelevant fingerprints without post-processing.

Embodiments of the invention can be useful for predicting adverse drug reactions based upon chemical structure. Embodiments of the invention can also be used for other tasks in drug design, such as prediction of drug-drug interactions.