 Research
 Open Access
 Published:
SSELMneg: spherical searchbased extreme learning machine for drug–target interaction prediction
BMC Bioinformatics volume 24, Article number: 38 (2023)
Abstract
Background
The experimental verification of a drug discovery process is expensive and timeconsuming. Therefore, efficiently and effectively identifying drug–target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed.
Methods
In this study, we propose a framework called SSELMneg for DTI prediction, in which we use a screening approach to choose highquality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine.
Results
The results demonstrated that the proposed technique outperformed other stateoftheart methods in 10fold crossvalidation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, Gprotein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively.
Conclusion
The screening approach produced highquality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other stateoftheart methods.
Introduction
Drug–target interaction (DTI) prediction is an important way to reposition drugs [1,2,3,4] that not only plays a crucial role in the development of new drugs [5] but is also essential for studying the adverse reactions of drugs [6, 7]. However, it is timeconsuming and expensive to verify DTIs using wet experimental methods [8, 9]. An important issue is how to reduce the cost of drug development. Thus, the in silico approach becomes essential because it improves the accuracy of finding drug–target relationships and saves time [10]. With the increasing number of public databases [11], different computational strategies can be more effectively applied for DTI prediction [12].
Generally, computational methods for DTI identification can be divided into three categories: ligandbased methods [13], dockingbased methods [13], and chemogenomic methods [14]. These methods have played an important role in predicting DTIs; however, docking methods that use the threedimensional (3D) structures of drugs and proteins, and then perform simulations to determine whether they interact, because of the limited 3D crystal structure of known targets, so there are limitations [15,16,17]. Ligandbased methods are based on the fact that similar molecules tend to have similar properties and usually bind similar proteins [18], which means that when the number of known ligands per protein is insufficient, the prediction results of ligandbased methods may become unreliable [19].
Chemogenomic methods use both drug and target information to integrate the chemical space of the drug and the protein space of the target into a pharmacological space to predict DTIs. An advantage of chemogenomic approaches is that they can work with widely abundant biological data to perform prediction [14]. We classify the chemogenomic methods into four types: machine learning methods, matrix factorization methods, networkbased methods, and hybrid methods.
There are three branches of machine learning methods for predicting DTIs: similaritybased methods, deep learning methods, and feature selection methods. Similarity/distancebased methods mainly use intersample similarity or distance [20,21,22]. Yamanishi et al. [23] developed a bipartite graph model to predict DTIs using a supervised approach to learn known drug–target relationships [24, 25]. Buza et al. proposed ECkNN/HLM, which is a Knearest neighbor (KNN) method (hubaware regression technique) with error correction, to mitigate the harmful effects of bad hubs [26,27,28]. Mei et al. proposed BLMNII, which is an inference integrated into a BLM approach, to solve a new candidate problem for pure BLM [29]. However, the main disadvantage of this set of methods is that only a few drugs and their interactions are known, and there is a large amount of unlabeled data in the dataset [30]. The application of deep learning methods in drug discovery has been increasing because of their excellent performance [31, 32]. Wen et al. proposed DeepDTIs, using DBN [33] to extract raw input vectors and predict new DTIs between FDAapproved drugs and targets [34]. Lee et al. proposed DeepConvDTI, which is a deep learning method, to obtain local residue patterns of proteins involved in DTI [35]. You et al. proposed LASSODNN, which is a deep learning method based on features extracted from LASSO regression models using proteinspecific features and drugspecific features for fitting [36]. The disadvantage of such methods is how to select truly noninteracting drug–target pairs [37]. Featurebased methods are currently the vast majority of machine learning methods that perform DTI prediction. They comprise a broad range of methods, including the support vector machine (SVM), treebased methods, and other kernelbased methods. SVM, KSVM, MHSVM, and other methods have been proposed. The main principle is that an SVM constructs one or a set of hyperplanes, which can be used to predict whether there is an interaction between a drug and target [19, 38,39,40,41,42]. Xia et al. proposed NetLapRLS, which is an improved version of LapRLS, by incorporating new kernels built from known DTI networks [43]. However, the problem encountered by such methods is that the lack of 3D structure of membrane proteins hinders the extraction of key features.
Matrix factorization methods have achieved better results in DTI prediction. GRMFWGRMF is a twomanifold learner for extracting lowdimensional nonlinear manifolds of DTI bipartite graphs proposed by Ezzat et al. [44]. Gönen et al. proposed a method to decompose the interaction score matrix into a kernel matrix (similarity matrix), which can be used as DTI predictors for the new drug and protein KBMF2K [45]. The disadvantage of this type of approach is that the rapid growth in the amount and variety of data related to a drug and/or target far exceeds the capabilities of matrixbased data representations and many current analysis algorithms. The networkbased approach uses graphbased techniques to perform DTI prediction, which has the advantage of being simple and reliable. Luo et al. proposed DTINet, which is a computational network integration pipeline for DTI prediction [46]. Chen et al. proposed NRRRH, which is a latent DTI inference method for bipartite graph networks based on the random walk with restart (RWR) framework [47]. The RWR proposed by Seal et al. is a method that requires matrix inversion and provides a good correlation score between two nodes in a DTIweighted graph [48].
Hybrid methods refer to all methods that use any combination of featurebased methods, matrix factorization, deep learning, and networkbased methods. Domain tunedhybrid proposed by Alaimo et al. is an extended NBI technique that combines domainbased knowledge, such as drug similarity and target similarity [49]. By reviewing the above methods, we found that a key problem is how to select negative samples; hence, the first problem we solve in this study is to establish a highly reliable negative sample dataset to overcome the shortcomings of previous methods. The number of negative samples is much larger than the number of positive samples. There will be a class imbalance problem, which will affect the prediction accuracy of the final DTI. Therefore, in this study, we choose a screening method to build a highly reliable negative sample dataset to solve the class imbalance problem.
Additionally, we propose a new classifier for predicting DTIs: an extreme learning machine (ELM) based on spherical search (SS) optimization. An ELM is a popular machine learning method that has been widely applied to realworld problems because of its fast training speed and good generalization performance [50]. Previously, scholars have used an ELM to predict the new relationship between drugs and targets. However, the network parameters are randomly generated, which reduces the prediction performance of the ELM model. Therefore, using the swarm intelligence algorithm to optimize the network parameters of the ELM is necessary. SS is a swarm intelligence algorithm that has few adjustment parameters; its accuracy, convergence rate, proficiency, and effectiveness are at an advanced level; and it has projection characteristics, which can eliminate stagnation during the search process, which is conducive to eliminating sticky in local minima.
Therefore, we propose a framework called SSELMneg for predicting the DTI. The innovations in this study are as follows:

1
We propose a DTI prediction framework using the screening approach and SSbased ELM.

2
We form a highconfidence negative sample dataset using a screening approach based on the principle that dissimilarity between a new drug and a drug with a known (predicted) protein precludes its possible correlation with the protein.

3
We propose an SSbased ELM. We optimize the parameters of the ELM using SS to improve the classification performance of DTIs.
Related work
In this study, we focus on machine learning methods to predict DTIs. Currently, this has three main problems: (1) complexity of training sample generation; (2) generation of credible negative samples; and (3) performance of the classifier.
The method used to train sample generation for machine learning is divided into a raw data generation method (featurebased method) and data integrated method using similarity scores (similaritybased method). The featurebased method requires feature selection; hence, it requires the drug–target pairs to be explicitly represented as fixedlength feature vectors, which can lead to a large number of complex calculations. By contrast, similaritybased methods do not require feature extraction or selection and are simpler to compute than that. The principle of the similaritybased DTI prediction method is to generate the similarity matrix of drugs by calculating the chemical structure of drugs and the similarity matrix of targets by calculating the characteristic of proteins, and finally, these two similarity matrices are used in various classification methods, such as [51].
However, whether featurebased methods or similaritybased methods are used to generate training sets, the number of negative samples far exceeds the number of positive samples because generally, unrecognized DTIs are considered as negative samples. This leads to data imbalance, which greatly reduces the accuracy of the classifier. The traditional method is to extract negative samples randomly. In recent years, some methods (not more) for extracting negative samples have been proposed. Mohammad et al. proposed the BRNS algorithm to extract balanced and reliable negative samples [52]. Jiaying You et al. [53] proposed a novel method to select the most likely negative DTIs. The assumption of this method is based on “guiltbyassociation,” which indicates that similar drugs may share similar targets and vice versa. However, these methods are often more complex to calculate. Therefore, in this study, we use a simpler screening method to extract a more credible negative sample based on the study of Liu et al. [42].
Additionally, the performance of the classifier is particularly important in machine learningbased methods for predicting DTIs, and more classical classifiers, such as SVMs [54,55,56], KNN [57, 58], and random forest [59, 60], have been used. The ELM has received a great amount of attention because of its excellent performance, and is also used in many areas [61,62,63], such as power and finance. Xin et al. [64] used ELMs for drugdrug interaction prediction, and An et al. [65] used kernel ELMs to identify DTIs based on drug fingerprints and protein evolutionary information. To date, few studies have been conducted in which researchers have used ELMs for the prediction of DTIs. One important reason is that the configuration of the hidden layer parameters of an ELM network requires better optimization methods.
Preliminaries
ELM is an algorithm proposed by Huang et al. [66] for solving a single hidden layer feedforward neural network. It initializes and randomly generates input weights and hidden layer biases, and uses a nonlinear activation function to map the input data to the new feature space. Its advantages are that it can minimize the training error, obtain the smallest weight norm and best generalization performance, and the learning speed is fast.
The number of input samples is N and the samples are \((x_i,t_i)\), where \(x_i=[x_{i1},x_{i2},\ldots ,x_{in}]^T \in R^n\) and \(t_i=[t_{i1},t_{i2},\ldots ,t_{in}]^T \in R^m\). The weights of the output layer are represented by the generalized inverse of the output matrix of the hidden layer. Hence, the ELM is expressed as
where L is the number of hidden layer nodes, \(w_i=[w_{i1},w_{i2},\ldots ,w_{in} ]^T\) is the weight vector that connects the input layer and hidden layer, \(b_i=[b_{i1},b_{i2},\ldots ,b_{in}]^T\) is the bias vector of the hidden layer, and \(\beta _i=[\beta _{i1},\beta _{i2},\ldots ,\beta _{im}]^T\) is the weight vector that connects the hidden layer and output layer. \(G(x)=[g(x,w_1,b_1),g(x,w_2,b_2),\ldots ,g(x,w_n,b_n )]\) represents the activation of the hidden layer function.
The learning goal of the single hidden layer neural network is to minimize the error of the output. When the error between the output result and sample N is zero, the above formula can be abbreviated as
where
where H is the output of the hidden layer node, \(\beta\) is the output weight, and T is the expected output. After applying the Moore–Penrose generalized inverse operation, we obtain
where \(H^\dag\) is the generalized inverse of matrix H.
Methods
Problem description
Our problem is based on the assumption that there is a drug set \(D \in \{d_1,d_2,\ldots ,d_n\}\) and protein set \(P \in \{p_1,p_2,\ldots ,p_n\}\), where D contains n drugs and P contains m proteins. The relationship between the drug and target protein is defined as an \(m\times n\) binary matrix Y, where the drug interacts with protein \(p_j\), \(y_{ij}=1\); when drug \(d_i\) does not interact with target protein \(p_j\); or the interaction is unknown. The similarity between drugs is represented by matrix \(S_d\) and the similarity between target proteins is represented by matrix \(S_P\). We calculated the prediction scores for each noninteracting drug–target pair and predicted new drug–target pairs.
Construct the negative sample set
The number of negative samples (unverified samples) in the drug–targeted interaction dataset was significantly higher than the number of positive samples (verified samples, as shown in Fig. 1a), which resulted in a decrease of the predictive performance of classification for drug–targeted interaction because of the data imbalance. To balance the dataset, in previous studies, researchers frequently used random selection methods to extract negative samples that were consistent with the size of the positive samples, as shown in Fig. 1b. However, this overlooks a critical issue: unlabeled DTIs may have interactions that have not been discovered or argued for. The random selection of negative samples may result in choosing some unlabeled DTI samples as negative samples; however, they are probably positive samples, which reduces the performance of the model. The proposed screening approach is to extract highquality negative samples. (These negative samples are far away from the positive samples, as shown in Fig. 1c.) We set all known DTI labels to 1 and all other chosen samples in the DTI space (drug–target pairs with no known interactions) to 0. We directly include all samples with labels of 1 in the dataset as positive samples and use all samples with labels of 0 as negative samples.
Building the assembly K of the known/predicted DTIs as mentioned above, and using the protein dissimilarity rule and drug dissimilarity rule [42], we integrate similarities between drugs into a drug composite similarity score, as is the case for similarities between proteins. This can be represented by \((c_k,p_j,d_{kj})\), where \(c_k\) represents drug k, \(p_j\) represents protein j, and \(d_{kj}\) represents the interaction between drug \(c_k\) and protein \(p_j\). For any protein \(p_l\) targeted by \(c_k\) in K, we compute the weighted score \(spc_{jkl}=w_{kl}*PS_{jl}\) that indicates the possibility that protein \(p_j\) and each known/predicted protein \(p_l\) are targeted by drug \(c_k\), that is, \((c_k,p_l,w_{kl} \in K)\). We calculate the combined score by summing the weighted scores \(spc_{jkl}\) with respect to l and thus obtain
Similarly, we compute the weighted score \(scp_{kj}=w_{ij}*CS_{ik}\) that represents the possibility that drug \(c_k\) targets \(p_j\) in consideration of the similarity between \(c_k\) and each known/predicted drug \(c_i\) that targets protein \(p_j\), that is, \((c_i,p_j,w_{kl}) \in K\). We calculate the combined score by summing the weighted scores \(spc_{kji}\) with respect to i and thus obtain
where \(p_{kj}\) is the interaction value between protein \(p_j\) and drug \(c_k\), and\(scp_{kji}\) is the similarity between drug \(c_{k}\) and all others.
For target drug \(c_k\) and protein \(p_j\), the average weighted score is defined as
We choose the potential negative samples according to the sorted scores obtained from Eq. (8), and those with the lowest scores form the negative sample candidate set. We combine the positive samples and negative samples to obtain the train dataset and test dataset. We conducted the experiments in this study on this dataset. The dataset (DTI pairs) are represented as
where \(C_i\) denotes the drug, \(P_i\) denotes the protein, and \(D_{ij}\) denotes the classification label (0 or 1) between drug \(C_i\) and protein \(P_i\).
In Fig. 1a, yellow circles represent known drug target pairs and gray triangles represent unknown or unrelated drug target pairs. The closer the gray triangles to the yaxis, the greater the likelihood of interrelationships. Figure 1b shows the randomly selected negative samples, which are represented by black triangles. The black triangles close to the left of the red line probably have DTI and they are probably positive samples, but they were chosen as negative samples. Figure 1c shows the negative samples selected using the screening approach, and the black triangles are far from the red line.
Extreme learning machine based on spherical search
The evolutionary algorithm is an optimization method that can be used to solve general optimization problems because it is simple and flexible, has no derivatives, and avoids falling into a local optimum [67]. The SS is an evolutionary algorithm for solving nonlinear bounded constrained global optimization problems [67]. To date, it has not been used to solve the parameter optimization problem of an ELM. In this study, we use it to optimize the network parameters of an ELM. First, we initialize the population of the SS algorithm using random selection. We define the population in the Gth iteration as \(Q_x\), which is expressed as
where \(x_{i,G}\) is the solution in the population, \(x_{ij}\) is the jth element of the ith solution, and \(x_{ij}\) is a parameter of the ELM. \(x_{i,G}\) is a vector in the Ddimensional search space. D denotes the number of parameters in the ELM.
Initialization of the solution: Choose a random distribution between the upper and lower dimensions of the jth element to initialize the solution as
where \(x_{uj}\) and \(x_{lj}\) are the upper and lower dimensional boundaries of the jth element, respectively. rand(0, 1] represents the generation of uniformly distributed random numbers in (0, 1].
Generation of trial solutions: Trial solutions are new potential solutions generated through iteration and competition:
where \(m_{i,G}\) is a projection matrix that determines the value of \(y_{i,G}\) on the \(D1\) dimensional spherical boundary; different \(p_{i,G}\) result in different \(y_{i,G}\) values:
where A is an orthogonal matrix,
where b is a binary vector, and
The position of \(y_{i,G}\) determines the spherical boundary of dimension \(D1\), and \(x_{i,G}\) is a specific solution. \(c_{i,G}\) represents the step size control vector, which is randomly calculated in [0.5, 0.7].
\(z_{i,G}\) represents the search direction. In optimization algorithms, the quality of new solutions is highly dependent on the balance between the exploration and utilization of the search space. We use two search operations: \(towardsbest\) and \(towardsrand\). We use the \(towardsrand\) method in the half of the population with a better solution because it has a better search ability, and use the \(towardsbest\) method in the other half because it has a better search ability. The combination of the two search directions provides a balance for the exploration and utilization of the search space, which not only improves the diversity of better solutions but also forces poor solutions to improve fitness:
where \(p_i\), \(q_i\), and \(r_i\) are the index numbers randomly selected from 1 to N, and \(x_{pbesti,G}\) is a randomly selected individual using the top p optimal solutions. \(x_{pbesti,G}\) and \(x_{pi,G}\) represent target points. \((x_qr_r)\) is the difference term, and \(x_q\) and \(r_r\) are randomly selected individuals from the current solution set; hence, the actual search direction may deviate from the target search direction, to a certain extent.
We use Success Historybasedcontrol Parameter Adaptation (SHPA) [68] to adapt two control parameters during the search: rank and \(c_i\). SHPA creates a history matrix L of size \((2 \times H)\) to hold H entries for the two control parameters, that is, the learning values \(l_r\) and \(l_c\) for parameters rank and c, respectively, in the last H iterations.
\(rank_{i,g}\) and \(c_{i,g}\) are calculated as
where Binornd represents the binomial distribution, j is chosen independently from the columns of matrix L, and each i is random. Cauchyrand represents the Cauchy distribution, j is chosen independently from the columns of matrix L, and each i is random.
The performance of SS is highly dependent on the control parameters \(c_i\), and the rank and size of population N [67]. In this study, we use the exponential population size reduction method to dynamically adjust the population size during the iterative process. We exponentially reduce the population as a function of the number of iterations by continuously reducing the population to match the exponential function. The population size is \(N_{init}\) at the first iteration and \(N_{min}\) at the final iteration. We use the following formula to calculate the size of the population for iteration \(N_{G+1}\):
where \(N_{min}=4\), \(nfes_{max}\) is the maximum number of function evaluations allowed. Whenever \(N_{G+1}<n_G\), we remove the \((N_GN_{G+1})\) worstranked individual from the population.
The calculation formulas of \(l_r\) and \(l_c\) are
Vectors \(S_r\) and \(S_c\) denote the rank and c containing successful trials, respectively. \(\left S_{r,g} \right\) and \(\left S_{c,g} \right\) represent the lengths of \(S_{r,g}\) and \(S_{c,g}\), respectively.
Selection of a new population for the next iteration:
We use greedy selection to update the new population set of the next generation. If the objective function value \(f(y_{i,g})\) of the trial solution is not higher than the objective function value \(f(x_{i,g})\) of the solution, then \(y_i\) replaces \(x_i\).
Fitness function:
where L is the number of hidden layer nodes, \(x_i=[x_{i1}, x_{i2},\ldots ,x_{in} ]^T\) is the weight vector that connects the input layer and hidden layer, \(x_b=[b_{i1},b_{i2},\ldots ,b_{in} ]^T\) is the bias vector of the hidden layer, and \(\beta _i=[\beta _{i1},\beta _{i2},\ldots ,\beta _{im} ]^T\) is the weight vector that connects the hidden layer and output layer. \(\beta _i\) can be computed using Eq. (5).
\(G(x)=[g(DTI_i,x_1,x_{b1}),g(DTI_i,x_2,x_{b2} ),\ldots ,g(DTI_i,x_n,x_{bn})]\) represents the activation of the hidden layer function:
\(AUC_i\) is the area under the receiver operating characteristic (ROC) curve (AUC) obtained using the ELM and \(AUPR_i\) is the area under the precisionrecall curve (AUCPR) obtained using ELM.
In this study, a DTI pair is input into ELM, that is, drug similarity, protein similarity, and known (or unknown) DTIs are input into ELM, and the predicted new drug–target relationships are output. In SSELMneg, the connection weight \(x_j\) between the input layer and hidden layer, and the bias \(x_b\) of the hidden layer are produced using the SS approach, and determine the connection weight between the hidden layer and output layer. The SS approach generates network parameters to enhance the prediction accuracy and generalization ability of the network. In this study, we use 10fold crossvalidation to verify the prediction performance of SSELMneg.
Our proposed framework is shown in Fig. 2 and the pseudocode is presented in Algorithm 1.
Experimental evaluation
Dataset
We compared the performance of our model on the gold standard dataset compiled by Yamanishi [23] with previous excellent methods to demonstrate the effectiveness of our approach. These datasets, derived from databases such as DRUG BANK and Kyoto Encyclopedia of Genes and Genomes 8 (KEGG), correspond to the DTIs of four important protein targets, that is, (i) enzyme (E); (ii) ion channel (IC); (iii) Gproteincoupled receptor (GPCR); and (iv) nuclear receptor (NR), and include 932 drugs, 989 target proteins, and 5,127 mutual relationships between the drugs. In this gold standard dataset, the known DTIs are from multiple public databases, including DrugBank [69], SuperTarget [70], KEGG BRITE [71], and BRENDA [72]. We obtained the similarity between drugs by integrating the chemical structure similarity of the drugs. We downloaded the chemical structures of the drugs from the KEGG LIGAND [71] database, and calculated the similarity using SIMCOMP [73]. We obtained the similarity between proteins by integrating the protein amino acid sequence similarity, which we downloaded from the KEGG GENES database. We obtained the similarity between proteins by integrating the protein amino acid sequence similarity, which we downloaded from the KEGG GENES database [74]. Table 1 presents some statistics for this dataset, including the total number of drugs, total number of targets, and total number of interactions. On average, there are more interactions per drug and target in ICs and Es than in GPCRs and NRs. The details of the gold standard dataset are in Table 1.
After the previous step of establishing a highconfidence negative sample set, we transformed the four interaction datasets into matrix form for the information description: (i) positive interaction and (ii) negative interaction.
Performance evaluation of DTIs
The proposed SSELMneg model aims to enhance the predictive ability of DTI. In our experiments, we evaluated the predictive capability of the SSELMneg model on Es, ICs, GPCR, and NRs on the gold standard dataset, and the SSELMneg model achieved reliable predictive performance. To ensure fairness, we used 10 crossvalidation tests to evaluate the performance of SSELMneg. We divided the gold standard dataset into 10 subsets of equal size. Next, we selected a subset as the test subset to evaluate the prediction results, and used the remaining 9 subsets to train the model. We repeated this process 10 times, each time using a different subset as the test subset. Finally, we obtained the average results from 10 folds. The evaluation metrics are the AUC and area under the precisionrecall curve (AUPR). We calculated the ROC curves as shown in Fig. 3 and used AUC as the main quality measure. A precisionrecall curve is a graph of the true positive rate (TPR) among all positive predictions for each given recall, and the AUPR value provides a quantitative estimate. The AUPR is suitable for assessing the performance of each method and provides a better estimate of quality because it penalizes the presence of false positives more severely than AUC:
The ROC space defines the false positive rate (FPR) as the xaxis and the TPR as the yaxis. The TPR is the ratio of all samples that are actually positive that were correctly judged as positive. The FPR is the ratio of all samples that are actually negative that were wrongly judged as positive.
Comparison with other methods
To further illustrate the robustness and effectiveness of the proposed method, we selected four classical methods and four new methods from recent years for comparison: BigramPSSM [41], iDTIESBoost [75], NRLMF [76], BLMNII [24], SELFBLM [77], NetLapRLS [43], SPLCMF [78], and WNNGIP [79]. To fairly compare DTI prediction performance, we applied these methods to the same gold standard dataset. We also used a randomized setup with 10fold crossvalidation, the same evaluation criteria, and the best parameters for each method. For SSELMneg, the maximum number of iterations MaxNfes = 10,000, greedy PbestRate = 0.11, population size \(PopSize=100\), \(rd=0.5\), \(c=0.7\), \(A_r=1.4\), and historical memory storage size \(Ms=5\). The parameters used for the other methods are mentioned in their corresponding articles. For BLMNII, \(g=max\) and \(\alpha =0.5\). For SELFBLM, \(c=1\) and \(\gamma =1\). For the details of specific parameters, please refer to the original articles.
Table 2 shows the AUC results for each method on the gold standard dataset and Table 3 shows the AUPR results. In these tables, the best results are shown in bold. As shown in Tables 2 and 3, SSELMneg achieved significantly improved AUC and AUPR performance compared with previous work. The AUPRs for SSELMneg on E, GPCR, IC, and NR were 0.9652, 0.9906, 0.9762, and 0.9455, respectively, which were higher than those for other advanced algorithms.
Figure 3(left) shows that on GPCR, the AUCs for SSELMneg were 12%, 6.1%, 2.4%, 2.7%, 5.1%, 4.9%, 8.9%, and 9.9% higher than those for BigramPSSM, iDTIESBoot, NRLMF, BLMNII, S PLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.993 vs 0.872, 0.932, 0.969, 0.966, 0.942, 0.944, 0.904, and 0.894, respectively). On NR, the AUCs for SSELMneg were 10%, 4%, 1.9%, 5.2%, 14.1%, 1.6%, 12.5%, and 19.6% higher than those for BigramPSSM, iDTIESBoot, NRLMF, BLMNII, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM (0.969 vs 0.869, 0.929, 0.950, 0.917, 0.828, 0.901, 0.844, and 0.773). On IC, the AUCs for SSELMneg were slightly lower than that for NRLMF (0.988 vs 0.989), but still better than those for the other methods. They were 9.9%, 5.1%, 0.4%, 0.7%, 2.9%, 3.2%, and 6.3% higher than those for BigramPSSM, iDTIESBoot, BLMNII, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.988 vs 0.889, 0.937, 0.984, 0.981, 0.959, 0.956, and 0.925, respectively). For Es, our model narrowly outperformed BLMNII by 0.01% (0.986 vs 0.985), was slightly lower than NRLMF (0.986 vs 0.987), but still far outperformed other models; our model was 3.8%, 1.7%, 1.6%, 2.2%, 1.7%, and 12.6% higher than BigramPSSM, iDTIESBoot, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.986 vs 0.948, 0.969, 0.970, 0.964, 0.969, and 0.860, respectively). Compared with our model, the stateoftheart algorithms all had higher AUCs on the E dataset, because it contains the largest number of known DTIs in Es.
Figure 3(right) shows that on the E dataset, the AUPR values for SSELMneg were 43.6%, 30.2%, 9%, 11.3%, 10.1%, 27.6%, 19.6%, and 34.3% better than those for BigramPSSM, iDTIESBoot, NRLMF, BLMNII, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.982 vs 0.546, 0.680, 0.892, 0.869, 0.881, 0.706, 0.786, and 0.639, respectively). On GPCR, the AUPR values for SSELMneg were 70.9%, 49.1%, 24.2%, 28.2%, 23.7%, 47.1%, 37.4%, and 39.2% higher than those for BigramPSSM, iDTIESBoot, NRLMF, BLMNII, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.991 vs 0.282, 0.500, 0.749, 0.709, 0.754, 0.520, 0.617, and 0.599, respectively). On IC, the AUPR values for SSELMneg were 59.2%, 50.2%, 7.6%, 7.3%, 4.4%, 26.5%, 16.2%, and 23.8% higher than those for BigramPSSM, iDTIESBoot, NRLMF, BLMNII, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.982 vs 0.390, 0.480, 0.906, 0.909, 0.938, 0.717, 0.820, 0.744, respectively). On NR, the AUPR values for SSELMneg were 53.5%, 24.5%, 24.5%, 24.5%, 42.7%, 35.7%, 48.3%, and 48.9% higher than those for BigramPSSM, iDTIESBoot, NRLMF,BLMNII, SPLCMF, WNNGIP, NetLapRLS, and SELFBLM, respectively (0.946 vs 0.411,0.701, 0.701, 0.701, 0.533, 0.589, 0.463, and 0.457, respectively).
In the four datasets, the average number of interactions between each drug and target was largest in ICs and smallest in NRs. This indicates that the interaction network of ICs contains more information than the interaction network of NRs; hence, the network similarity of ICs is higher and more informative than the network similarity of NRs. NRs contain the largest proportion of ’new drug candidates,’ whereas ICs contain the smallest proportion.
Table 4 shows the results of comparing the AUCs for the different methods using the Friedman test, with our method performing the best. Table 5 shows that there was a significant difference between the performance of the methods.
Table 6 shows the results of the comparison of the AUPRs for the methods using the Friedman test, with our method also performing best. Table 7 shows that there was a significant difference between the performance of the methods.
According to the comparative results of the Friedman test, SSELMneg was the best (as shown in Tables 4 and 6).
Predicting novel interactions
To further demonstrate the ability of SSELMneg to predict a new DTI, we input all the negative samples into SSELMneg as a test set to predict possible new DTIs. There were no known interactions in the test dataset; hence, we ranked the predicted high DTI scores (possibly positive interactions, but not validated yet) according to their scores, and placed the predicted high scoring interactions in medical biological databases and scientific literature for manual ranking, including DrugBank, KEGG, PubChem, and STITCH. The ROC results of the interaction prediction on the dataset are shown in Fig. 4, the prediction results with interaction after validation are listed in Table 8, and the validation method is marked in the evidence column.
The dataset that we used was compiled by Yamanishi. The drug–target interactions contained in the E, IC, GPCR, and NR datasets were extracted from KEGG several years ago, and to allow for a comparison of prediction techniques, they have not been changed [26].
However, with the development of technology, increasing numbers of DTIs have been validated experimentally and their results updated in various biological databases. Therefore, we can compare predicted new interactions in various international public databases. If the predicted new interaction is included in KEGG, DrugBank, or other databases, then we consider the interaction to be valid.
Table 8 shows that our method found many valid interactions, such as Interaction of Aripiprazole (D01164) with 5hydroxytryptamine receptor 1B(hsa3351); Interaction of Diazoxide with calcium voltagegated channel subunit alpha1 I; Interaction of Diazoxide with calcium voltagegated channel subunit alpha1 G; and Progesterone (D00066), Norethindrone (D00182), Levonorgestrel (D00950), and Norgestrel (D00954) all target androgen receptor (hsa367).
The synthetic progestins used to date for contraception and menopausal hormone therapy are derived either from testosterone (19nortestosterone derivatives) or progesterone (17OH progesterone derivatives and 19norprogesterone derivatives). Among the 19nortestosterone derivatives, the estrane group includes norethisterone and its metabolites, and the gonane group includes levonorgestrel and its derivatives [80]. Aripiprazole (OPC14597) is a novel atypical antipsychotic drug that is reported to be a highaffinity D2dopamine receptor partial agonist [81]. It has moderate affinity for the 5hydroxytryptamine receptor 1B receptor, \(6< pKi < 7\) [82].
Discussion and conclusion
In this study, we proposed a swarm intelligence algorithmbased method for optimizing ELMs called SSELMneg by integrating drugdrug similarity, proteinprotein similarity, and the drugprotein interaction relationship for novel drugprotein interaction predictions. We established a highly credible negative sample dataset, which effectively solved the class imbalance problem between positive and negative samples. We also demonstrated the superior performance of SSELMneg using results obtained by predicting human DTI networks involving Es, ICs, GPCRs, and NRs.
A small molecule is a type of low molecular weight organic compound with a variety of biological functions. In recent years, mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment [4]. Chen et al. built a computing model of Bounded Nuclear Norm Regularization for SM–miRNA Associations prediction, in which a heterogeneous SM–miRNA network was constructed using miRNA similarity, and a matrix representing the heterogeneous network was defined. Wang et al. [83, 84] proposed a novel method called DualNetwork Collaborative Matrix Factorization for predicting potential SM–miRNA associations [85]. These methods use the similarity matrix of miRNAs, and our method uses the similarity matrix of coding proteins; hence, we believe that it is feasible to improve our method to apply the theory of miRNAs. Drug–target binding affinity prediction is also a research direction for our future work. CHEN et al. proposed a new model called molecular representation blockbased drug–target binding affinity prediction (MRBDTA) [86], which showed superior performance in predicting the binding affinity between replicationassociated proteins of severe acute respiratory syndrome coronavirus 2 (SARSCoV2). In future work, we will focus on predicting the relationship between miRNAs and drugs and on predicting drug target binding affinity.
Machine learningbased methods are used to identify novel DTIs. However, the performance and robustness of this method is datadependent; hence, inherent knowledge and limited negative samples severely limit the performance of this computational method. In our study, we used drug dissimilarity rules and protein dissimilarity rules to score negative samples, and excluded negative samples with low scores, that is, negative samples that may have interactions between drugs and proteins but have not been verified. Thus, we built a highconfidence and classbalanced train dataset for our SSELM model. An ELM is a popular machine learning method that has been widely used in realworld problems because of its fast training speed and good generalization performance. However, in an ELM, randomly assigned input weights and hidden biases often degrade generalization performance. In this study, we assigned input weights and hidden biases using the SS approach to provide the optimized parameters of an ELM. Therefore, it is very suitable to find the optimal network parameters of ELM.
Finally, we input the negative samples that were selected by applying rules to the training set into SSELMneg. The experimental results verified that our method performed best in terms of identifying DTIs. In the future, we will focus on swarm intelligence optimization for the classifier for the prediction of DTIs.
Availability of data and materials
The datasets generated and/or analyzed during the study are available at http://web.kuicr.kyotou.ac.jp/supp/yoshi/drugtarget/.
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Acknowledgements
We gratefully acknowledge all the people who helped in the establishment of the drug–target dataset. We thank Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (61976239); and the Natural Science Foundation of Guangdong Province, China (2020A1515010783).
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Lingzhi Hu, Chengzhou Fu, and Deyu Tang wrote the main manuscript, and Zhonglu Ren, Yongming Cai, and Jin Yang prepared the datasets for the experiment. Lingzhi Hu, Chengzhou Fu, Siwen Xu, and Wenhua Xu conducted the experiment. All authors reviewed the manuscript.
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Hu, L., Fu, C., Ren, Z. et al. SSELMneg: spherical searchbased extreme learning machine for drug–target interaction prediction. BMC Bioinformatics 24, 38 (2023). https://doi.org/10.1186/s1285902305153y
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DOI: https://doi.org/10.1186/s1285902305153y
Keywords
 Drug–target interactions
 Drug discovery
 Extreme learning machine
 Spherical search
 Class imbalance