Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations
© The Author(s) 2016
Received: 11 December 2015
Accepted: 23 June 2016
Published: 1 July 2016
Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery.
In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance.
The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.
Drugs can bind different protein targets in the human organism. This action in multiple targets is responsible for therapeutic effects along with clinical adverse effects. For this reason, improvement in the identification of drug-target interactions is of great importance in the discovery of additional applications for drugs already in the market, also called drug repurposing, and in drug safety through the explanation of undesirable adverse effects caused by drugs administration. From the initial discovery stages to the final approval in the pharmaceutical market, molecules have to pass through many evaluation steps with the consequent high associated costs and failure risks . The estimated cost to develop a new drug until commercialization can reach 1 billion [2, 3]. However, drug repurposing strategies can decrease the overall time and cost since existing drugs have been already studied from the point of view of safety and pharmacokinetic profiles . Discovery of new targets for existing drugs is also important in drug safety since supplies valuable information about possible mechanism of action of adverse drug effects (ADEs) .
In the last years, different computational methods have been developed to discover new drug-protein interactions . Molecular similarity has been widely applied in medicinal chemistry to discover molecules that bind a specific target . However, similarity can be determined using different measurements. Molecules can be compared based on their 2D molecular structure [8, 9]. Keiser et al.  showed the usefulness of comparing molecular fingerprints to generate an approach called SEA (Similarity Ensemble Approach) with great potential in the prediction of new targets. The authors showed that targets can be predicted according to the similarity based on their ligands and discovered new potential applications for existing drugs [10, 11]. On the other hand, 3D molecular structure comparison offers also great potential in medicinal chemistry and drug discovery [12, 13]. It has been shown that both 3D and 2D molecular structure analysis provide different abilities to capture diverse structural patterns related with biological activities [14, 15]. Other types of molecular similarities have also provided great insights in drug-target discovery. Campillos et al.  used adverse drug reactions profiles to develop a target identification model validated experimentally. Nevertheless, exploiting clinical data of the disease constitutes another example of a system to identify new targets related to drugs . Some bioinformatics methodologies compared drugs based on gene expression profiles in microarrays and yielded associations between drugs, targets, pathways and diseases [17–21]. Integration of heterogeneous chemical and biological data into predictive models was also a successful strategy in the detection of new targets, indications and adverse effects [22–26]. In summary, different similarity measures and methods have been published with important applications in drug-target identification and hence, drug repurposing and drug safety .
On the other hand, drug similarity has also been applied to identify directly associations between drugs and adverse effects. As an example, 2D and 3D structure similarity modeling was previously implemented in the detection of drug candidates responsible for adverse effects [28–30]. Other types of studies with great applications in drug safety and pharmacovigilance have shown potential in drug-adverse effect detection through data mining of the scientific literature  or pharmacovilance databases [32–34], such as Electronic Health Records or the FDA Adverse Event Reporting System . The availability of big heterogeneous data sources combined with the explosion of computational methods encourages the large-scale study of relationships between drugs, targets and adverse effects.
Results for the 3D predictor were compared with a 2D model. Both methods performed similarly and yielded ROC curves greater than 0.80 (see Additional file 3: Figure S2). However, as it was shown previously, 3D structure methods captured a diverse chemical space compared to 2D techniques and can generate different sets of candidates [14, 30, 39]. Previous research showed chemical-biological relationships captured by 3D molecular structure methods and not detected by 2D methods, and vice versa. To prove the potential of detecting a different chemical space, we have plotted in Additional file 4: Figure S3 the 10 % top scored drug–drug similarities in a matrix of drugs using both approaches. Some drug pair examples are detected by 3D methods and not detected according to 2D approaches and vice versa.
The target-phenotype model was validated using two external reference standards of known associations between proteins and adverse reactions. A database generated in a previous study  by surveying the scientific literature to find target-adverse effect associations and manually verified was used as a validation set (49 target-adverse effects). A second reference standard of 42 target-adverse effects was taken into account and extracted from the DART database (Drug Adverse Reaction Target Database) . Both test sets are provided in Additional file 6: Table S2. We labeled the known associations as true positives within the whole set generated by our model and calculated the area under the ROC curve for the external tests (AUROCs were 0.70 and 0.71 for the Kuhn and DART tests respectively). More detailed results of our validation process, including sensitivity and specificity at different thresholds, are provided in Additional files 7 and 8: Tables S3 and S4. The q-values calculated for the target-adverse effect associations included in the reference standards were lower than the q-values in the model background (see Fig. 4b). Our system prioritized the true positive cases over the complete set of target-adverse effect associations. For the next implementation step, a final set of 2426 target-adverse effect candidates was selected with an EF > 5 and q < 0.05 and at least 3 drugs in common in both protein and adverse effect (Additional file 9: Table S5 contains the list of 2426 target-adverse effects with EF and q-values).
Linkage of drug-targets and target-adverse effects
Leveraging drug-targets with drug-phenotype
Examples of some drug-target candidates generated by our predictor
Similar drug in ChEMBL (ATC category)b
Drug candidate (ATC category)
EF and q-valuesd
Diclofenac (antiinflammatory agent, non-steroid)
Carbamazepine (carboxamide deriv., antiepileptic)
EF = 3.17 q < .05
Phenytoin (hydantoin deriv., antiepileptic)
EF = 2.71 q < .05
Ondansetron (serotonin antagonist, antiemetic-antinauseant)
Molindone (indole deriv., antipsychotic)
EF = 17.73 q < .05
Oxymetazoline (descongestant, sympathomimetic)
Molindone (indole deriv., antipsychotic)
EF = 22.16 q < .05
Oxybuprocaine (local anesthetic)
DNA repair protein RAD52 homolog
EF = 6.57 q < .05
Niclosamide (salicylic acid deriv., anticestodal)
Tyrosine-protein kinase SRC
EF = 2.75 q < .05
Diethyltryptamine (psychedelic drug)
EF = 8.21 q < .05
Pentamidine (agent against Leishmaniasis/Trypanosomiasis)
Haloperidol (antipsychotic, butyrophenone deriv.)
Muscarinic acetylcholine M4
EF = 11.22 q < .05
Leveraging drug-adverse effects with drug-target data
We compared the performance of the 3D drug-adverse effect model by itself with the 3D drug-adverse effect model leveraged with target data. Precision and EF in different top positions are shown in Fig. 6b, c. Precision was improved in different top positions when the data is leveraged with target information. However, precision decreases until reach a similar value in the final position 1294 (0.43 for 3D drug-adverse effect model leveraged with phenotypic data and 0.36 for the 3D drug-adverse effect model by itself). Implementing target data into drug-adverse effect candidates enhanced also identification of drug-adverse effect associations.
We have developed a method that integrates 3D structural similarity, protein interactions and adverse effects, in a large scale multi drug-target-adverse effect predictor with novel implications in drug repurposing and patient safety. We also provided a leveraging system to better prioritize the selected drug-target associations through the application of drug-phenotypic data. In the opposite way, improvement in the detection of drug-adverse effects was achieved integrating drug-target data from ChEMBL. We have shown that integrating drug-targets with drug-phenotype data and vice versa is very useful to enhance the performance of the predictors.
Our drug-target predictor scores the candidates based on the maximum 3D similarity against the set of drugs known to bind the protein. This system allows for each drug-target candidate isolating the drug that cause the signaling score and analyze all the information associated, such as type and conditions of the biological assay, protein organism or even different reported activities. The 3D pharmacophoric approach can associate as similar two drugs that belong to the same pharmacological category. However, it also allows the detection of pairs of drugs that are classified in different pharmacological classes. Additional file 11: Figure S5 shows the Anatomical Therapeutic Chemical (ATC)  relationship between 1000 random pairs of drugs detected within the threshold of 0.75 for the 3D scoring, along with a histogram of the distribution of the cases. Drugs associated with a high score have the tendency of belonging to the same ATC class. However, as the 3D scores decreases we found more pairs of drugs with different pharmacological profiles.
In the generation of 3D drug similarity data, it is possible to use alternative methodologies, such as different drug conformational analysis, molecular alignments or 3D similarity functions. In our conformational analysis protocol and due to simplicity reasons, only the global minimum energy structure for each drug was retained. However, a more complex approach can be taken into account retaining more conformations for each drug to better represent the bioactive bound conformation. Previous studies by our research group showed that although a set of conformations could describe better the bound form of drugs, the global minimum energy structures yielded also good root mean squared deviations (RMSDs) against crystallized drugs bound to the targets . We collected a set of 158 co-crystallized drug structures in our data from the Protein Data Bank and compared them to: (1) the minimum energy 3D structure generated by our MCMM calculations, (2) the top10 minimum energy conformations extracted from the MCMM (the best RMSD against the crystal is selected). Additional file 12: Figure S6 shows the RMSDs calculated in the comparison. The average RMSD values are 1.66 and 1.05 for both protocols, respectively. Our protocol, taking into account only the minimum energy conformation, is simpler and showed good performance in the recovery of co-crystallized drugs (122 out of 158 presented a RMSD lower than 2.5).
3D pharmacophoric similarity
We downloaded the dataset of drugs available in DrugBank . We did not include proteins, large peptides and drugs with more than 200 atoms due to the complexity to calculate the 3D most stable conformation of molecules with high degree of freedom. DrugBank also provided specified chiral centers information determining bioactive conformation of drugs. Our dataset included 1526 drugs that were pre-processed with LigPrep . This module generated protonation states at neutral pH and a maximum of three enantiomers in the case of lack of chirality information for some centers. Initial molecular geometry was also optimized using OPLS_2005 force field.
Monte Carlo Multiple Minimum (MCMM) conformational analysis
We carried out a MCMM conformational analysis for the drugs using Macromodel from Schrödinger . We used water as implicit solvent in the calculation to generate more extended conformations representing with higher fidelity biological active conformations. Non-bonded cut-off distances for H-bond, van der Waals and electrostatic forces were set to 4.0, 8.0 and 20.0 Å respectively. Although different minimum energy structures can be studied, we retained only the OPLS_2005 global minimum energy structure as representative of the calculation to simplify next modeling stages.
We used ChEMBL database  as a source of protein data, including pharmacological targets, off-targets, enzymes and transporters. Drugs from DrugBank  were mapped to the ChEMBL data using a combination of drug name, InChI keys, and smiles codes resulting in a set of 1526 drugs by which target data was downloaded. Target information in the database was pre-processed as a previous step before data integration in the predictor. This step included incorporation of repeated drug-target cases into a unique case (different bioassays referring to the same target were clustered); elimination of biological data not well specified, such as cases labeled as “not determined”, “not active”, “not tested”, “no inhibition”, “potential missing data”, etc., or drug-target cases with low affinity or potency, i.e. cases where IC50, EC50 or K i was greater than 50 µM. Unspecified cases where the potency was only determined with a threshold,such as “EC50 greater than” were also eliminated from the initial data. Additional information, such as assay details was also retained and included in each drug-target case. To increase data robustness, only targets with at least 5 associated drugs were considered in the modeling. Final drug-target data comprised 22,838 drug-target associations (positive controls) with 1526 drugs and 726 targets (1,107,876 possible combinations).
We used SIDER  as a resource of 99,423 drug-adverse effect associations (4192 adverse effects related to 996 drugs) extracted from package inserts and public documents. SIDER database is an important source of adverse effect information, although some adverse reactions would need additional confirmation through more studies.
Drug-target predictor: 3D drug similarity and target integration
Target-phenotype predictor: target and adverse effect data integration
In a similar way described by Kuhn et al. , we integrated drug-phenotypic data from SIDER with drug-target data extracted from ChEMBL to detect overrepresentations of protein-adverse effects (see Fig. 4a). Since the aim is the detection of targets that cause clinical adverse effects, only human proteins in ChEMBL were integrated in SIDER adverse effect data. After mapping our initial 1526 drugs with drugs in SIDER and with drugs with human targets in ChEMBL data, we found 842 drugs by which phenotypic and target data was combined. Targets and adverse effects associated with less than five drugs were not considered in the analysis. Our final data included 347 targets and 1773 adverse effects (615,231 possible target-ADE associations). Enrichment factor (EF) and p values (Fisher’s exact test) were calculated for each target-adverse effect combination taking into account number of drugs associated with both target and adverse effect (TP), number of drugs that only bind the target (FP), drugs only associated to the adverse effect (FN), and number of drugs not associated with neither of them (TN). Since multiple associations are taken into account and following the protocol described by Kuhn et al. , we addressed multiple hypotheses by using q-values calculated with the “qvalue” package in R  instead of raw p-values. Modeling was validated through the evaluation of two independent test sets of target-adverse effects associations: (1) the Kuhn database, extracted in a previous study  from the scientific literature and manually verified and (2) the DART database (Drug Adverse Reaction Target Database) . AUROCs, sensitivity, specificity, precision and enrichment factor at different top thresholds were provided as a comparative measurement.
Integration of drug-target and target-adverse effect predictors
Final modeling was performed through the integration of previous models, the drug-target and the target-adverse effect predictors. A set of 178,385 drug-target associations with a 3D score threshold of 0.75 was selected as candidates. Regarding the target-adverse effect predictor, we selected 2426 target-adverse effects with EF > 5, q-value <0.05 and at least 3 drugs in common in both target and adverse effect. Both sets of signals were intersected to extract a final set of 38,181 drug-targets associated with multiple adverse effects (drug-target-multiADEs). Considering drug-target-adverse effects as unique cases the number of data points is 338,638.
Leveraging drug-protein interactions with phenotype data
In the set of 38,181 drug-target associations (3D score ≥0.75 and with multiple associated adverse effects), we calculated enrichment factors (EFs) and q-values (multiple testing using the “q value” package in R) based on TP (adverse effects corroborated in SIDER for the drug), FP (adverse effects not found in SIDER), FN (adverse effects found in SIDER but not predicted in the modeling), and TN (adverse effects that are not predicted by our model and they are not found in SIDER either). Performance in a set of 921 drug-target associations with an EF > 1 and q-value <0.05 was compared to sets extracted from the drug-target model by itself.
Leveraging drug-adverse effect associations with target data
Associations with a 3D score ≥0.75 between our drugs and adverse effects were extracted from a previous model reported by our research group . In a similar way as described previously, drug-adverse effects were linked to the 2426 target-adverse effect associations to generate a set of 100,713 drug-adverse effects associated to different targets. Enrichment factors (EFs) and q-values were calculated for each drug-adverse effect association using target information: TP (predicted targets validated in ChEMBL), FP (predicted targets not validated in ChEMBL), FN (targets present in ChEMBL for the drug that are not predicted by our modeling) and TN (targets not predicted and not described in ChEMBL). A set of 1294 drug-adverse effects with an EF > 1 and q-value <0.05 were selected.
SV and GH wrote the manuscript. SV and GH designed the research. SV performed the research. SV analyzed the data. SV and GH contributed new reagents/analytical tools. All authors read and approved the final manuscript.
This study was supported by Grant R01 LM006910 (GH) “Discovering and Applying Knowledge in Clinical Databases” from the U.S. National Library of Medicine.
The authors declare that they have no competing interests.
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