 Research article
 Open access
 Published:
Choquet integralbased fuzzy molecular characterizations: when global definitions are computed from the dependency among atom/bond contributions (LOVIs/LOEIs)
Journal of Cheminformatics volume 10, Article number: 51 (2018)
Abstract
Background
Several topological (2D) and geometric (3D) molecular descriptors (MDs) are calculated from local vertex/edge invariants (LOVIs/LOEIs) by performing an aggregation process. To this end, norm, mean and statisticbased (nonfuzzy) operators are used, under the assumption that LOVIs/LOEIs are independent (orthogonal) values of one another. These operators are based on additive and/or linear measures and, consequently, they cannot be used to encode information from interrelated criteria. Thus, as LOVIs/LOEIs are not orthogonal values, then nonadditive (fuzzy) measures can be used to encode the interrelation among them.
Results
General approaches to compute fuzzy 2D/3DMDs from the contribution of each atom (LOVIs) or covalent bond (LOEIs) within a molecule are proposed, by using the Choquet integral as fuzzy aggregation operator. The Choquet integralbased operator is rather different from the other operators often used for the 2D/3DMDs calculation. It performs a reordering step to fuse the LOVIs/LOEIs according to their magnitudes and, in addition, it considers the interrelation among them through a fuzzy measure. With this operator, fuzzy definitions can be derived from traditional or recent MDs; for instance, fuzzy Randiclike connectivity indices, fuzzy Balabanlike indices, fuzzy Kier–Hall connectivity indices, among others. To demonstrate the feasibility of using this operator, the QuBiLSMIDAS 3DMDs were used as study case and, as a result, a module was built into the corresponding software to compute them (http://tomocomd.com/qubilsmidas). Thus, it is the only software reported in the literature that can be employed to determine Choquet integralbased fuzzy MDs. Moreover, regression models were created on eight chemical datasets. In this way, a comparison between the results achieved by the models based on the nonfuzzy QuBiLSMIDAS 3DMDs with regard to the ones achieved by the models based on the fuzzy QuBiLSMIDAS 3DMDs was made. As a result, the models built with the fuzzy QuBiLSMIDAS 3DMDs achieved the best performance, which was statistically corroborated through the Wilcoxon signedrank test.
Conclusions
All in all, it can be concluded that the Choquet integral constitutes a prominent alternative to compute fuzzy 2D/3DMDs from LOVIs/LOEIs. In this way, better characterizations of the compounds can be obtained, which will be ultimately useful in enhancing the modelling ability of existing traditional 2D/3DMDs.
Introduction
In several application areas, mainly in the multicriteria decisionmaking, the information aggregation process is the main step to perform [1,2,3]. In such a process, the individual criteria are combined into a single value (global criterion), in such a way that all properties contained in each individual criterion are included or reflected in the global criterion, by using an aggregation operator [4, 5]. Thus, several aggregation operators may be used to obtain different global criteria. In this way, decisionmakers could consider diversity of criteria with the purpose of making the best final decision. Traditional aggregation operators, where individual criteria are considered as values independent of one another, are those most frequently employed (e.g. OWAlike functions [6,7,8]). These operators are based on linear and/or additive measures and, thus, they are not suitable to deal with the dependency among criteria.
The dependency or interaction among criteria is an intrinsic feature present in the decisionmaking tasks in the real world. For example, if various work teams are analyzed to select the one with the best teamwork, and with this purpose the efficiency of each worker belonging to a same team is measured, then the efficiency of each team to do teamwork is not the sum of the individual efficiencies, but the interaction among its workers to achieve the best teamwork. Therefore, it is most suitable to use nonadditive measurebased operators, instead of traditional operators, for an approximate modeling of people’s assessment practices. In this sense, the concept of fuzzy (nonadditive) measure, also known as capacity, was introduced by Sugeno, in order to model the importance of a coalition within a set of interrelated criteria [9].
According to Lebesgue’s philosophy [10], once a measure is defined, it is possible to obtain an integral with regard to that measure. Thus, associated with the concept of fuzzy measure, there is the concept of fuzzy integral [11, 12], being the Choquet integral one of the most popular [13,14,15,16,17]. The Choquet integral constitutes a generalization of the Lebesgue integral [10], as well as of other traditional operators (e.g. OWAlike functions), due to the fact that they coincide when the measure used is additive. The Choquet integral has been successfully used in several applications, such as: face recognition [18], rulebased systems [19], data mining [20] and decisionmaking [21,22,23]. The success of the Choquet integral as aggregation operator is due to, as already pointed out, its ability of including dependency among criteria by means of a fuzzy measure [14, 15].
One of the applications of the aggregation operators is in the chemical structures encoding. This process constitutes an essential step to perform several studies in the cheminformatics field, such as molecular similarity [24] and quantitative structure–activity relationships (QSAR) [25,26,27]. The codification of chemical structures is performed by means of the molecular descriptors (MDs) calculation. The MDs are values computed from symbolic molecular representations, by applying different mathematical transformations [28] based on a wide variety of theories, such as quantum chemistry [29] and information theory [30]. The MDs are useful values in the sense that they can contribute to obtain a better comprehension on the interpretation of molecular properties, and/or they can integrate a model to predict biological activities in novel compounds [28].
As it can be seen in [28], several procedures to determine MDs are based on the calculation of Local Vertex Invariants (LOVIs) or Local Edge Invariants (LOEIs). These procedures perform an aggregation process on the LOVIs/LOEIs computed to determine the final value (MD) that characterizes the molecular structure (e.g. Randić–Razinger index [31] and local Balaban index [32]). The LOVIs and LOEIs depict each atom (vertex) and covalent bond (edge) of a molecule, respectively. They are computed from graphbased molecular representations without depending on any atom/bond numbering, nor on the rotation and translation of the molecules. LOVIs/LOEIs are represented into ndimensional vectors, where n denotes the total number of atoms/bonds. The summation, summation of squares, min and max are the operators often used to obtain global MDs from LOVIs/LOEIs, being the summation operator the one most commonly used.
However, as it has already been pointed out, the use of different aggregation operators yields diversity of global criteria, that is why decisionmakers consider several alternatives to make the best final possible decision. Thus, if the MDs whose calculation is based on the aggregation of atom/bond contributions (LOVIs/LOEIs) are determined using several operators, then diversity of global characterizations of the molecules can be computed. For instance, if on the wellknown LOVIs called vertex degree, the kurtosis function and the traditional OWA operator are applied, then an aggregation indicating the tailedness of this LOVIs vector, and a weighted aggregation, giving more importance to the vertices (atoms) with the highest degrees, can be calculated, respectively. Both examples are quite different from the common use of the summation operator.
Inspired on this idea, recent strategies to compute topological (2D) and geometric (3D) MDs from atom/bond contributions (LOVIs/LOEIs) have been introduced [33,34,35,36,37,38]. These 2D/3DMDs employ aggregation operators based on Minkowski norms (e.g. Euclidean norm), central tendency statistics (e.g. arithmetic mean) and dispersion statistics (e.g. kurtosis). As it has been confirmed elsewhere [39,40,41], the use of these operators contribute to obtain global 2D/3DMDs with better information content (variability) and linear independence (orthogonality) than other 2D/3DMDs reported in the literature. In addition, the diversity of the 2D/3DMDs computed with these operators allowed to achieve successful outcomes in comparative modeling tasks [42, 43], as well as in several practical applications [44,45,46].
Nonetheless, up to date, the global 2D/3DMDs computation from atom/bond contributions (LOVIs/LOEIs) is based on additive operators, under the assumption that these contributions are noninterrelated values. However, it is wellknown that the biological activities or properties of the compounds do not only depend on the molecular shape, but also on the interactions that are often noncovalent in nature. Thus, nonadditive (fuzzy) measurebased aggregation operators (e.g. Choquet integral) may be used, with the purpose of obtaining an approximate characterization of the interrelation that each atom (or bond) has, regarding the other ones. In this way, 2D/3DMDs orthogonal to the other existing ones may be obtained, because of the fuzzy basis of their computations.
To the best of our knowledge, only two fuzzy MD types have been introduced: (1) by using pharmacophorebased molecular similarity [47], and (2) by using the number of interposed bonds as the measure of separation among atoms depicting pharmacophore kinds (2DFPT MDs) [48, 49]. Therefore, fuzzy 2D/3DMDs computed through an aggregation process on atom/bond contributions (LOVIs/LOEIs) have not been reported to date. Consequently, this work is aimed at introducing a different way for the global 2D/3DMDs computation from LOVIs/LOEIs, by using the Choquet integral as fuzzy aggregation operator. This report is planned as follows. Second section defines some concepts regarding the fuzzy measures and the Choquet integral. Third section presents the adaptation of several procedures to compute fuzzy MDs. Fourth section presents a practical example. Fifth section studies the feasibility of using this approach. Last section describes the main findings and conclusions.
Background of fuzzy measures and Choquet integral
Definition of fuzzy measure and singleton measure. L_{mδ}measure: fuzzy measure composed of maximized Lmeasure and Deltameasure
The fuzzy measures (or capacities) are functions that determine a weight considering the interrelation (or dependency) among criteria within a subset [11, 14]. Formally, let a universal set \(X = \left\{ {x_{1} ,x_{2} , \ldots ,x_{N} } \right\}\) and \(P\left( X \right)\) be the power set of \(X\), \(P\left( X \right) = 2^{N}\), then a fuzzy measure [9] or capacity [13] on \(X\) is a set function \(\mu\): \(P\left( X \right) \to \left[ {0,\,1} \right]\) that fulfils the following axioms:

1.
\(\mu \left( \emptyset \right) = 0\) (lower boundary condition).

2.
\(\mu \left( X \right) = 1\) (upper boundary condition).

3.
\(If\,A,\,B\, \in \,P\left( X \right)\, \wedge \,A\, \subseteq B\, \Rightarrow \,\mu \left( A \right) \le \mu \left( B \right)\) (monotonicity).
Therefore, for any \(A \subseteq X\), \(\mu \left( A \right)\) can be considered as the degree of importance (or weight) of the combination \(A\) of criteria. If \(\left A \right = 1\), then \(\mu \left( A \right) = \mu \left( {x_{i} } \right)\), and it constitutes the traditional weight when element \(x_{i}\) is considered separately. It is important to highlight that \(\mu \left( {x_{i} } \right)\) is denominated as fuzzy density or singleton measure, denoted as \(s\left( {x_{i} } \right)\), when any \(A \subseteq X\) has a single element \(x_{i}\). Moreover, a fuzzy measure is additive if \(\mu \left( {A \cup B} \right) = \,\mu \left( A \right) + \mu \left( B \right)\), whenever \(A \cap B = \emptyset\). Thus, it is enough to determine \(\forall \,x_{i} \in X\) the corresponding \(s\left( {x_{i} } \right)\) to define the measure completely. Other important properties of the fuzzy measures are the superadditivity and subadditivity. The former indicates high synergy or cooperative action among the criteria of a set, while the latter expresses the opposite. So, the additivity can be interpreted as the no interaction among the criteria of a set.
Several fuzzy measures have been reported in the literature, such as the Sugeno λmeasure [9] (that was the first one proposed), the Pmeasure [50], the Shapley values [51], the korder fuzzy measure [52], among others [53,54,55]. The λmeasure and Pmeasure are among the most widely employed. The λmeasure is not a closed form [9], whereas the Pmeasure is not sensitive enough, because it only determines the max value of the input set [50] (see Additional file 1). In addition, when the number of criteria is large, then the computation of the λ parameter is quite complex in the λmeasure, because a polynomial equation of higher order must be resolved. In order to tackle these drawbacks, a fuzzy measure comprised of the Maximized Lmeasure (L_{m}measure) [56, 57] and Deltameasure (δmeasure) [58], denoted as L_{mδ}measure, was proposed by Liu et al. [54, 59].
Formally, a fuzzy L_{mδ}measure, \(g_{{L_{m\delta } }}\), on finite set \(X = \left\{ {x_{1} ,x_{2} , \ldots ,x_{N} } \right\}\) is defined as follows:
where \(A \subseteq X\), \(L \in \left[ {  1,\,\infty } \right)\), \(s\left( \cdot \right)\) is a singleton measure for each \(x_{i} \in X\), \(\sum\nolimits_{x \in X} {s(x)} = 1\), \(g_{{L_{m\delta } }} \left( \emptyset \right) = 0\) and \(g_{{L_{m\delta } }} \left( X \right) = 1\). This fuzzy measure satisfies the following properties: (1) L_{mδ}measure is an increasing function on \(L\); (2) if \(L =  1\), then L_{mδ}measure is just the Pmeasure; (3) if \(L = 0\), then L_{mδ}measure is additive (it coincides with the λmeasure when \(\lambda = 0\)—see Additional file 1); (4) if \( 1 < L < 0\), then L_{mδ}measure satisfies the subadditivity property (low synergism); and (5) if \(0 < L < \infty\), then L_{mδ}measure satisfies the superadditivity property (high synergism).
Mathematical definition of the Choquet integral
The Choquet integral was first presented in capacity theory [13]. Its use as an integral with regard to fuzzy measures was then introduced by Hohle [60] and, it was later rediscovered by Murofushi and Sugeno [61, 62]. This integral, as an nplace operator, has been used in several works [18,19,20,21,22,23], in order to fuse information when interrelated criteria are accounted for. Formally, let a finite set \(X = \left\{ {x_{1} ,\,x_{2} ,\, \ldots ,\,x_{N} } \right\}X \in \Re_{ \ge 0}^{N}\) and \(\mu\) be a fuzzy measure on \(N\), then the Choquet integral of \(X\) with respect to \(\mu\) is a function \(C_{\mu }\): \(\Re_{ \ge 0}^{N} \to \Re_{ \ge 0}\) according to the next expression:
where \(\left( \cdot \right)\) denotes a permutation on \(N\), so that \(x_{\left( 1 \right)} \ge x_{\left( 2 \right)} \ge \cdots \ge x_{\left( N \right)}\). That is, \(x_{\left( i \right)}\) is the ith largest value in the set \(\left\{ {x_{1} ,x_{2} , \ldots ,x_{N} } \right\}\). Thus, \(A_{\left( i \right)} = \left\{ {x_{\left( i \right)} , \ldots ,x_{\left( N \right)} } \right\}\) when \(i \ge 1\), and \(A_{0} = \emptyset\). So, for instance, if \(X = \left\{ {x_{1} ,\,x_{2} ,\,x_{3} } \right\}x_{2} \ge x_{3} \ge x_{1}\), then the Choquet integralbased aggregation is computed as follows:
As it can be seen, this operator performs a reordering of its arguments according to their magnitudes, as the OWAlike operators do [6, 7]. Indeed, as it has been demonstrated elsewhere [15, 63, 64], the Choquet integral constitutes a generalization of the latter. Moreover, it can be observed that since the values are ordered in decreasing order, then \(\mu \left( {A_{\left( i \right)} } \right) \ge \mu \left( {A_{{\left( {i  1} \right)}} } \right)\). Lastly, it is important to highlight that the Choquet integral fulfils some properties, such as: (1) it is a continuous function; (2) it is homogeneous of degree 1; (3) it is monotonic and idempotent, if and only if \(\mu\) is a fuzzy measure; and (4) it is compensative when \(\mu\) is a normalized fuzzy measure.
Extending traditional functions to derive Choquet integralbased fuzzy descriptors
Table G3 in [28] shows several traditional functions to derive classic 2D/3DMDs from atom/bond contributions (LOVIs/LOEIs), e.g. the Zagreb indices [65], the Balabanlike indices [66], the Wienertype indices [67], among others [28]. As it can be observed, these functions are mainly based on the summation and product aggregation operators. Consequently, the functions described in Table G3 do not consider the possible interrelation among LOVIs/LOEIs, which are only aggregated of linear and/or additive ways. Therefore, in order to consider the dependency among LOVIs/LOEIs, some of these traditional functions can be extended to compute fuzzy 2D/3DMDs (FMDs), by using the Choquet integral \(\left( {C_{\mu } } \right)\) with respect to a fuzzy measure \(\mu\) as shown below:
where \(L_{i}\) and \(L_{j}\) are the LOVI values for any pair vertices \(v_{i}\) and \(v_{j}\) (atoms) of a molecular graph \(G\), \(A\) is the number of vertices, \(V\) represents the set of vertices of \(G\), \(a_{ij}\) denotes the coefficients of the adjacency matrix of \(G\) (1 for adjacent vertices, 0 otherwise), \(K\) is the total number of graph fragments to be considered, \(n_{k}\) is the number of vertices within the kth fragment, and \(\alpha^{'}\) and \(\lambda^{'}\) are two real parameters. The superscript in notation \(MD\) represents the numbering used to identify these functions in Table G3 in [28]. Accordingly, this numbering is also used to identify the corresponding fuzzy formulations \(\left( {FMD_{{C_{\mu } }} } \right)\). Note that these definitions can also be used to compute fuzzy 2D/3DMDs from LOEIs in place of LOVIs.
From these fuzzy formulations, several specific descriptors can be computed, for instance: (1) from \(FMD_{{C_{\mu } }}^{1}\) for \(\alpha^{'} = \lambda^{'} = 1\), fuzzy DIVATI MDs [41], fuzzy GTSTAF MDs [34] and fuzzy QuBiLSMAS MDs [37] can be obtained, when the LOVIs vector is computed with some of those families; (2) if vector \(L\) is computed with the vertex degree invariant, then from \(FMD_{{C_{\mu } }}^{1}\) for \(\alpha^{'} = 1\) and \(\lambda^{'} = 2\), from \(FMD_{{C_{\mu } }}^{4}\) for \(\alpha^{'} = \lambda^{'} = 1\) and from \(FMD_{{C_{\mu } }}^{4}\) for \(\alpha^{'} = 1\) and \(\lambda^{'} =  {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern0pt} \!\lower0.7ex\hbox{$2$}}\), the fuzzy first Zagreb index [65], the fuzzy second Zagreb index [65] and the fuzzy Randic connectivity index [68] can be obtained, respectively; (3) from \(FMD_{{C_{\mu } }}^{4}\) for \(\alpha^{'} = \frac{B}{C + 1}\) (B is the number of graph edges (covalent edges) and C is the number of rings) and \(\lambda^{'} =  {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern0pt} \!\lower0.7ex\hbox{$2$}}\), the fuzzy Balabanlike indices can be determined [32]; (4) from \(FMD_{{C_{\mu } }}^{6}\) for \(\alpha^{'} = 1\) and \(\lambda^{'} =  {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern0pt} \!\lower0.7ex\hbox{$2$}}\), the fuzzy Kier–Hall connectivity indices can be obtained [69]; and (5) from \(FMD_{{C_{\mu } }}^{7}\), fuzzy autocorrelation MDs can be computed. A practical example is presented below.
Practical example: Choquet integralbased fuzzy QuBiLSMIDAS molecular descriptors
Geometric multilinear algebraic MDs, also known as QuBiLSMIDAS, were introduced as a novel framework to characterize molecular structures [39, 40]. These 3DMDs are the only ones that encode structural information between two atoms of a molecule using several metrics (e.g. Soergel) [39], as well as chemical information corresponding to the relations between three and four atoms through multimetrics (e.g. bond and dihedral angle) [40]. QSAR studies on eight benchmark chemical datasets were carried out [43], where the QuBiLSMIDAS 3DMDs yielded significantly superior outcomes with respect to 12 2D/3DQSAR methodologies established in the literature. The QuBiLSMIDAS 3DMDs were also applied in the prediction of inhibitory activity of bromodomain modulators (BRD2, BRD3 and BRD4) with successful results [46].
Traditional (no fuzzy) definition of the QuBiLSMIDAS descriptors
The traditional QuBiLSMIDAS MDs are computed from atomlevel descriptors (LOVIs). Thus, the kth atomlevel twolinear \(\left[ {{}_{{ns\left( {ss,ds,mp} \right)}}^{{\left( * \right)\left( {NQ} \right)}} b_{\left( F \right)}^{a,k} \left( {\bar{x},\bar{y}} \right)} \right]\), threelinear \(\left[ {{}_{{ns\left( {ss,mp} \right)}}^{{\left( * \right)\left( {NQ} \right)}} tr_{\left( F \right)}^{a,k} \left( {\bar{x},\bar{y},\bar{z}} \right)} \right]\) and fourlinear \(\left[ {{}_{{ns\left( {ss,mp} \right)}}^{{\left( * \right)\left( {NQ} \right)}} qu_{\left( F \right)}^{a,k} \left( {\bar{x},\bar{y},\bar{z},\bar{w}} \right)} \right]\) QuBiLSMIDAS 3DMDs are calculated as Nlinear (multilinear) algebraic maps in \({\mathbb{R}}^{n}\), in a canonical basis set, when geometric coordinatebased relations among two \(\left( {N = 2} \right)\), three \(\left( {N = 3} \right)\) and four \(\left( {N = 4} \right)\) atoms are considered, respectively [39, 40]. The formulation (indicial notation) of these 3DMDs is as follows:
where n is the number of atoms, “a” is a particular atom \(\left( {a = 1, \ldots ,n} \right)\), the indices \(i,j,l,h =\) \(1 \ldots n\) denote the entries of the matrices and property vectors, \(k = \pm 1,\, \ldots ,\, \pm 12\) is the power of the matrices, and \(x^{1\left( * \right)} ,\, \ldots ,\,x^{n\left( * \right)}\), \(y^{1\left( * \right)} ,\, \ldots ,\,y^{n\left( * \right)}\), \(z^{1\left( * \right)} ,\, \ldots ,\,z^{n\left( * \right)}\) and \(w^{1\left( * \right)} ,\, \ldots ,\,w^{n\left( * \right)}\) are the coefficients of the property vectors \(\overline{{x^{\left( * \right)} }}\), \(\overline{{y^{\left( * \right)} }}\), \(\overline{{z^{\left( * \right)} }}\) and \(\overline{{w^{\left( * \right)} }}\), respectively, when central chirality aspects are codified (*) or not [70]). Moreover, \({}_{{ns\left( {ss,ds,mp} \right)}}^{{}} \left( {{{\mathbb{N}}{\mathbb{Q}}}} \right){\mathbb{G}}_{\left( F \right)}^{a,k}\), \({}_{{ns\left( {ss,mp} \right)}}^{{}} \left( {{{\mathbb{N}}{\mathbb{Q}}}} \right){{\mathbb{G}}{\mathbb{T}}}_{\left( F \right)}^{a,k}\) and \({}_{{ns\left( {ss,mp} \right)}}^{{}} \left( {{{\mathbb{N}}{\mathbb{Q}}}} \right){{\mathbb{G}}{\mathbb{Q}}}_{\left( F \right)}^{a,k}\) denote the two, three and fourtuple atomlevel matrices for each atom “a”, respectively. From these atomlevel matrices, then atomlevel descriptors (LOVI) are determined. Each LOVI constitutes an entry \(\left( {L_{a} } \right)\) in the corresponding vector of atomlevel descriptors \(L^{k}\) (LOVIs vector). The notations (\({{\mathbb{N}}{\mathbb{Q}}}\), F, ns, ss, ds and mp) between parentheses are not mandatory during the calculation and they will be explained below.
Keepall total matrices (\({\mathbb{G}}^{k}\), \({{\mathbb{G}}{\mathbb{T}}}^{k}\) and \({{\mathbb{G}}{\mathbb{Q}}}^{k}\)) are the basis to compute these 3DMDs. For \(k = 1\), the entries of the matrices \({\mathbb{G}}^{1}\), \({{\mathbb{G}}{\mathbb{T}}}^{1}\) and \({{\mathbb{G}}{\mathbb{Q}}}^{1}\) denote the information encoded for the relations between two, three and four atoms of a molecule, respectively, by using several metrics and multimetrics (see Tables 1–2 in [43]). From these matrices, neighborhoodquotient matrices (\({{\mathbb{N}}{\mathbb{Q}}{\mathbb{G}}}^{k}\), \({{\mathbb{N}}{\mathbb{Q}}{\mathbb{G}}{\mathbb{T}}}^{k}\) and \({{\mathbb{N}}{\mathbb{Q}}{\mathbb{G}}{\mathbb{Q}}}^{k}\)) may be obtained, which contain information of the interatomic relations that satisfy certain molecular cutoffs [71]. Localfragment matrices (\({\mathbb{G}}_{F}^{k}\), \({{\mathbb{G}}{\mathbb{T}}}_{F}^{k}\) and \({{\mathbb{G}}{\mathbb{Q}}}_{F}^{k}\)) may also be computed (see Equation 13 in [39] and Equations 17–18 in [40]) to encode information of chemical fragments or atomtypes (F) of interest. Normalized matrices may also be obtained using the simplestochastic (ss—see Equation 10 in [39] and Equations 13–14 in [40]), doublestochastic (ds) [72] and mutual probability (mp—see Equation 12 in [39] and Equation 15–16 in [40]) procedures. If no normalization procedure is used, then the matrices are nonstochastic (ns).
Finally, from the keepall (neighborhoodquotient) nonstochastic (simplestochastic, doublestochastic or mutualprobability) total (localfragment) matrices [\({}_{{ns\left( {ss,ds,mp} \right)}}^{{}} \left( {{{\mathbb{N}}{\mathbb{Q}}}} \right){\mathbb{G}}_{\left( F \right)}^{k}\), \({}_{{ns\left( {ss,mp} \right)}}^{{}} \left( {{{\mathbb{N}}{\mathbb{Q}}}} \right){{\mathbb{G}}{\mathbb{T}}}_{\left( F \right)}^{k}\) and \({}_{{ns\left( {ss,mp} \right)}}^{{}} \left( {{{\mathbb{N}}{\mathbb{Q}}}} \right){{\mathbb{G}}{\mathbb{Q}}}_{\left( F \right)}^{k}\)], the respective atomlevel matrices are calculated (see Equation 9 in [39] and Equations 3–4 in [40]) with the purpose of determining the vectors of LOVIs (see Eqs. 8–10). After that, and considering the atomlevel descriptors (LOVIs) as independent values of one another, then the (nonfuzzy) global kth twolinear, threelinear and fourlinear QuBiLSMIDAS 3DMDs are obtained using one or several (nonfuzzy) aggregation operators based on the Minkowski definition (e.g. Euclidean norm), central tendency statistics (e.g. harmonic mean) and dispersion statistics (e.g. variance) [39, 40].
Fuzzy definition of the QuBiLSMIDAS descriptors based on the Choquet integral
So far, QuBiLSMIDAS 3DMDs are computed from LOVIs considered as noninterrelated values. However, as already pointed out, the biological activities or properties of the compounds do not only depend on the molecular shape, but also on the interactions that are often noncovalent in nature. Therefore, the interrelation among atomic contributions (LOVIs) may be an aspect to consider during molecular encoding. In this way, from the corresponding LOVIs vector and considering their coefficients as interrelated values of one another, then the fuzzy global kth twolinear, threelinear and fourlinear QuBiLSMIDAS 3DMDs are computed using the definition of Choquet integral (see Eq. 2) as shown below:
where \(C_{\mu } ( \ldots )\) is the Choquet integral with respect to a fuzzy measure \(\mu\); and \({}_{{{}_{{ns\left( {ss,ds,mp} \right)}}^{{}} b\left( F \right)}}^{{\left( * \right)\left( {NQ} \right)}} L_{a}^{k}\), \({}_{{{}_{{ns\left( {ss,mp} \right)}}^{{}} tr\left( F \right)}}^{{\left( * \right)\left( {NQ} \right)}} L_{a}^{k}\) and \({}_{{{}_{{ns\left( {ss,mp} \right)}}^{{}} qu\left( F \right)}}^{{\left( * \right)\left( {NQ} \right)}} L_{a}^{k}\) are the kth twolinear, threelinear and fourlinear atomlevel descriptors (LOVIs), respectively, determined for each atom “a” of a molecule, according to Eqs. 8–10. Note that these formulations coincide with the definition \(FMD_{{C_{\mu } }}^{1}\) for \(\alpha^{'} = \lambda^{'} = 1\) (see Eq. 3). Scheme 1 shows a flowchart regarding the calculation of these fuzzy 3DMDs.
The L_{mδ}measure [54, 59] (see Eq. 1) is used to compute the importance (weight) of the interrelation among atomlevel descriptors (LOVIs) during the fuzzy QuBiLSMIDAS 3DMDs calculation. As it can be seen in Eq. 1, the L_{mδ}measure depends on a singleton measure \(s\left( x \right)\), so that \(\sum\nolimits_{x \in X} {s(x)} = 1\), being \(X\) a finite set of elements. In this case, set \(X\) is the LOVIs vector \(\left( {L^{k} } \right)\) computed for a compound, either the kth twolinear, threelinear or fourlinear atomlevel QuBiLSMIDAS 3DMDs. Therefore, the singleton measure \(s\left( {L_{a}^{k} } \right)\), \(\forall \,L_{a}^{k} \in L^{k}\), determines the belonging degree of the descriptor for atom “a” (\(L_{a}^{k}\)—see Eqs. 8–10) within the set of atomlevel descriptors \(\left( {L^{k} } \right)\). To this end, the following two functions [73] were used:
Aggregated Objects Type 1 (AO1):
Aggregated Objects Type 2 (AO2):
where \(\alpha \in \left[ {0,\,1} \right]\); and \(b_{i}\) constitutes the ith largest of the atomlevel descriptors \(L_{1}^{k} ,L_{2}^{k} , \ldots ,L_{n}^{k}\). As it can be analyzed, these functions calculate the belonging degree according to the magnitude of the atomlevel descriptors, that is, the highest belonging degrees are assigned to the highest atomlevel descriptors. The AO1 and AO2 functions are commonly used in the weightings computation for OWAlike operators [73]. However, they satisfy the same mathematical constraints as the singleton measures for the L_{mδ}measure and, thus, they were considered to compute the fuzzy densities in this work. Scheme 2 shows an example of the calculation of these fuzzy 3DMDs.
Lastly, the computation of these fuzzy global 3DMDs can be performed through the module built into the QuBiLSMIDAS software (http://tomocomd.com/qubilsmidas) [35]. As it can be seen in Fig. 1, in this module, the value of the Lparameter corresponding to the L_{mδ}measure (see Eq. 1), as well as the singleton measure to be used, can be customized. Several default configurations, determined according to their results in cheminformatics studies, are also provided. These fuzzy 3DMDs can also be obtained using the distributed computation module coupled to the heterogeneous and nondedicated Tarenal platform (http://tomocomd.com/tarenal) [74].
Performance of the Choquet integralbased fuzzy QuBiLSMIDAS molecular descriptors
This section is dedicated to demonstrating the feasibility of using the Choquet integral in the fuzzy MDs calculation, by using the QuBiLSMIDAS 3DMDs as study case. To this end, models based on the multiple linear regression (MLR) technique were built using the MobyDigs software, which uses the Genetic Algorithm (GA) metaheuristic as search method [75]. The leaveoneout cross validation \(\left( {Q_{loo}^{2} } \right)\) was used as the fitness function. The models retained for further validation were selected according to the best bootstrapping value \(\left( {Q_{boot}^{2} } \right)\). All the datasets were optimized with the CORINA software (https://www.mnam.com/products/corina). From now on, the ‘atomic contributions’ term is only to refer to the atomlevel QuBiLSMIDAS 3DMDs (LOVIs).
Configurations for the computation of fuzzy densities
This study is to determine the best configurations for the computation of fuzzy densities. To this end, two project groups with the same configuration of Choquet integralbased fuzzy QuBiLSMIDAS 3DMDs were built (see Additional file 2). In both project groups, the value of the αparameter of the functions AO1 (see Eq. 14) or AO2 (see Eq. 15) was varied into the interval \(\left[ {0,\,1} \right]\), with a step equal to 0.1. In this way, all the possible configurations were assessed. The value of the Lparameter of the L_{mδ}measure (see Eq. 1) was set to − 0.5 and 0.5, in order to determine the best configurations when a low and a high synergism among the atomic contributions is accounted for. The Cramer’s steroids set [76,77,78,79,80] is the one used to fulfill the goals of this study. This dataset is composed of 31 steroids, so that structures 1–21 and 22–31 belong to the training and test sets, respectively. Compound number 31 is left out at being outlier.
These projects on the steroid’s dataset were calculated (see Additional file 2 for SDF format). For each descriptors matrix obtained (see Additional file 3), models from 1 to 4 variables were created by using the GAMLR method, in order to predict the binding affinity to the CBG protein [81]. The statistical methods Yscrambling, bootstrapping, external validation and Fisher function were determined for each model, in order to create a data matrix \(M_{n\,x\,k}\), where the \(n\) rows and the \(k\) columns denote the statistics computed and the configurations to be compared, respectively (see Additional file 4). In this way, the rank (first step of the Friedman test [82]) for each configuration can be computed (see Additional file 5). The configurations selected as the best ones were those with a rank lesser than the difference between the average rank and the standard deviation calculated for a same group.
As a result, Fig. 2 shows the average bootstrapping \(\left( {Q_{boot}^{2} } \right)\) and external predictive \(\left( {Q_{ext}^{2} } \right)\) accuracies corresponding to the best configurations based on functions AO1 (see Eq. 14) and AO2 (see Eq. 15). On one hand, the configurations AO1 \(\left( {\alpha = 0.2} \right)\), AO1 \(\left( {\alpha = 0.3} \right)\), AO2 \(\left( {\alpha = 0.6} \right)\) and AO2 \(\left( {\alpha = 0.0} \right)\) are those with the best outcomes when a low synergism is considered during the fuzzy QuBiLSMIDAS 3DMDs calculation (see Fig. 2a). On the other hand, at considering a high synergism (see Fig. 2b), the configurations with the best behavior are AO1 \(\left( {\alpha = 0.8} \right)\), AO1 \(\left( {\alpha = 0.9} \right)\), AO1 \(\left( {\alpha = 0.2} \right)\), AO2 \(\left( {\alpha = 0.6} \right)\) and AO2 \(\left( {\alpha = 0.5} \right)\). In all cases, the average performance achieved by the models is suitable, at presenting \(Q_{boot}^{2} > 0.7\) and \(Q_{ext}^{2} > 0.6\).
Nonetheless, in a general sense, the configurations obtained for a high synergism (Fig. 2b) present a comparabletosuperior behavior with regard to the configurations obtained for a low synergism (Fig. 2a). These results suggest that the dependency among atomic contributions is an important aspect to consider during the molecular codification with the QuBiLSMIDAS 3DMDs. Thus, at least preliminarily, the methodological contribution of this report is justified, in which the 2D/3DMDs fuzzy calculation from LOVIs/LOEIs is presented. However, more studies must be carried out to prove the feasibility of this fuzzy approach with respect to the traditional approach, where the atom/bond contributions (LOVIs/LOEIs) are considered as noninterrelated values.
Performance of the Choquet integralbased (fuzzy) QuBiLSMIDAS descriptors versus norm, mean and statisticbased (nonfuzzy) QuBiLSMIDAS descriptors
In this section, QSAR models based on the QuBiLSMIDAS 3DMDs were built to assess the applicability of the Choquet integral in the fuzzy MDs computation from LOVIs/LOEIs. Note that the superiority of the QuBiLSMIDAS 3DMDs in modeling tasks was confirmed in [43], where its performance was assessed and compared with regard to 12 methodologies reported in the literature. Therefore, the current study is only devoted to performing an internal analysis among the models built with the fuzzy 3DMDs (based on Choquet integral) with respect to the models built with the nonfuzzy 3DMDs (based on traditional operators).
Chemical datasets to assess the performance between fuzzy and nonfuzzy QuBiLSMIDAS descriptors
Eight wellknown benchmark datasets were used to carry out this study. These datasets have been widely employed in the literature [83,84,85,86], including the analysis to assess the performance of the QuBiLSMIDAS MDs in QSAR [43]. The datasets are composed of angiotensin converting enzyme (ACE) inhibitors, acetylcholinesterase inhibitors (ACHE), ligands for the benzodiazepine receptor (BZR), cyclooxygenase2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), inhibitors of glycogen phosphorylase b (GPB), thermolysin inhibitors (THER) and thrombin inhibitors (THR). A description of these datasets is shown in Table 1, whereas Additional file 6 contains the corresponding SDF (Structure Data Format) files.
Methodology to assess the performance between fuzzy and nonfuzzy QuBiLSMIDAS descriptors
Three projects with a same configuration of nonfuzzy QuBiLSMIDAS 3DMDs were built, each of them using the norm, mean, statisticbased operators, respectively. Two other projects with the same previous configuration were also created but using the Choquet integral to determine the respective fuzzy 3DMDs. One of the Choquet integralbased projects was designed with the best fuzzy densities obtained for a low synergism among atomic contributions (see Fig. 2a), while the other project was planned with the best fuzzy densities for a high synergism (see Fig. 2b). The Lparameter of the L_{mδ}measure was set to − 0.25, − 0.5 and − 0.75 (subadditivity) in the project considering a low synergism, while the opposite values (superadditivity) were used in the other project. Additional file 6 shows the XML files of the projects described.
These projects on each chemical dataset mentioned above (see Table 1) were computed. Then, the best 1500 variables (MDs) according to the variability criterion [87] were retained by using the IMMAN software [88]. Posteriorly, the GAMLR procedure was used to build several models for 3, 5 and 7 variables for each operatortype. The best model for each dimension on each dataset was retained (Additional file 7). A pool with the nonfuzzy 3DMDs and other pool with the fuzzy 3DMDs included in the best models built on each dataset were created. From these pools, nonfuzzy and fuzzy models for 7 variables were built on each dataset, and the models with the best bootstrapping value were selected as the best ones (Additional file 8). The external validation \(\left( {Q_{ext}^{2} } \right)\) statistic parameter was computed for each model developed (see Additional file 7, Additional file 8).
The \(Q_{ext}^{2}\) values (predictive abilities) obtained for each chemical dataset were used to establish a comparison and statistical assessment between the models built with the fuzzy and nonfuzzy QuBiLSMIDAS 3DMDs, respectively. In this sense, an analysis by means of a boxplot graphic (boxandwhisker graphic) was firstly performed, in order to examine the shape of the distributions of the results achieved. Then, a Wilcoxon signedrank test [89] was carried out to know whether the predictive abilities achieved by the fuzzy models and the predictive abilities achieved by the nonfuzzy models differ. The SPSS software was used to perform the first analysis mentioned above, while the Keel [90] software was employed to perform the other one. A significance level \(\alpha = 0.05\) was accounted for. Note that the ‘fuzzy model’ and ‘nonfuzzy model’ terms are referred to the models built with the fuzzy and nonfuzzy QuBiLSMIDAS 3DMDs, respectively.
Analysis of the performance achieved by the fuzzy and nonfuzzy QuBiLSMIDAS descriptors
Figure 3 shows a comparative graphic of the average performance achieved by the models for 3, 5 and 7 variables, built with the fuzzy QuBiLSMIDAS 3DMDs, when a low (Lparameter < 0 in Eq. 1) and a high (Lparameter > 0 in Eq. 1) synergism among atomic contributions is accounted for. As it can be seen, the fuzzy 3DMDs calculated for a low synergism present the best behavior on ACE (\(\left( {Q_{ext}^{2} } \right)\) = 0.5667), BZR (\(\left( {Q_{ext}^{2} } \right)\) = 0.4052), COX2 (\(\left( {Q_{ext}^{2} } \right)\) = 0.2930) and GPB (\(\left( {Q_{ext}^{2} } \right)\) = 0.5931) datasets, while the fuzzy 3DMDs determined for a high synergism present the best behavior on ACHE (\(\left( {Q_{ext}^{2} } \right)\) = 0.4303), DHFR (\(\left( {Q_{ext}^{2} } \right)\) = 0.3446), THER (\(\left( {Q_{ext}^{2} } \right)\) = 0.4541) and THR (\(\left( {Q_{ext}^{2} } \right)\) = 0.3270) datasets. So, it is evidenced that fuzzy QuBiLSMIDAS MDs calculated both for a low and a high synergism contribute to codify useful chemical information, and that their performances depend on the molecular structures under study. Therefore, both types of fuzzy 3DMDs should be jointly used with the purpose of creating models with better predictive ability.
In this sense, Fig. 4 shows a plotting of the external predictive ability yielded by the models of 7 variables created with the fuzzy and nonfuzzy QuBiLSMIDAS 3DMDs, respectively. The fuzzy MDs determined for a low and a high synergism among atomic contributions were used, while the nonfuzzy MDs used are those computed from the norm, mean and statisticbased operators. It can be seen that, in all the chemical datasets employed, the models built with the fuzzy MDs [(ACE, \(\left( {Q_{ext}^{2} } \right)\) = 0.6103); (ACHE, \(\left( {Q_{ext}^{2} } \right)\) = 0.5231); (BZR, Q ^{2}_{ext} = 0.5400); (COX2, \(\left( {Q_{ext}^{2} } \right)\) = 0.3558); (DHFR, Q ^{2}_{ext} = 0.4638); (GPB, \(\left( {Q_{ext}^{2} } \right)\) = 0.6447); (THER, Q ^{2}_{ext} = 0.4569); (THR, \(\left( {Q_{ext}^{2} } \right)\) = 0.4072)] yield comparabletosuperior performances with regard to the models based on the nonfuzzy MDs [(ACE, \(\left( {Q_{ext}^{2} } \right)\) = 0.5629); (ACHE, \(\left( {Q_{ext}^{2} } \right)\) = 0.3887); (BZR, \(\left( {Q_{ext}^{2} } \right)\) = 0.5222); (COX2, \(\left( {Q_{ext}^{2} } \right)\) = 0.3387); (DHFR, \(\left( {Q_{ext}^{2} } \right)\) = 0.4390); (GPB, \(\left( {Q_{ext}^{2} } \right)\) = 0.6442); (THER, \(\left( {Q_{ext}^{2} } \right)\) = 0.4080); (THR, \(\left( {Q_{ext}^{2} } \right)\) = 0.3600)]. Thus, it can be stated that MDs with better modeling ability can be calculated using the Choquet integralbased operator, if compared with the MDs computed from the traditional (nonfuzzy) operators.
Moreover, Fig. 5a depicts the number of fuzzy QuBiLSMIDAS 3DMDs, both for a low and a high synergism among atomic contributions, included in the models built on each dataset. As it can be seen, the fuzzy QuBiLSMIDAS 3DMDs determined for a high synergism influenced on the external predictive power of all models developed, being the models corresponding to the ACHE and THR datasets exclusive of these MDs. It can also be noted that the model developed on the ACE dataset presents more QuBiLSMIDAS 3DMDs for a high synergism than for a low synergism. A likewise behavior is shown by the models built on the BZR, COX2, DHFR, GPB and THER datasets, but in these cases, there are more fuzzy MDs for a low synergism.
Additionally, Fig. 5b shows the Lparameter average value for the MDs included in the models. In general, it can be seen that, albeit the superadditivity is exclusive for the ACHE and THR datasets, the amount of superadditivity on each dataset is moderate. This behavior can be due to the fact that the datasets used are comprised of congeneric compounds. That is, since the compounds are structurally similar, then MDs computed of additive way, or considering a low synergism among atomic contributions, may be those in achieving better correlations into a QSAR model. This assumption is supported in the average amount of low synergism obtained. As it can be seen, the behavior for a low synergism is from moderate to high, except in the BZR dataset. Thus, at least preliminarily, it can be stated that high amounts of superadditivity will contribute to compute better MDs in noncongeneric datasets than in congeneric datasets.
Statistical analysis of the performance achieved by the fuzzy and nonfuzzy QuBiLSMIDAS descriptors
To carry out this analysis, the predictive abilities \(\left( {Q_{ext}^{2} } \right)\) achieved by the models built with the fuzzy and nonfuzzy QuBiLSMIDAS 3DMDs on each dataset were accounted for. Figure 6 shows the boxplot graphic corresponding to the \(Q_{ext}^{2}\) values obtained. Additional file 9 shows the descriptive statistics calculated. On one hand, it can be firstly seen that there are not outlier predictive abilities. In addition, it can be seen that the lowest \(Q_{ext}^{2}\) attained by the fuzzy models is better than the lowest \(Q_{ext}^{2}\) attained by the nonfuzzy models; while the highest outcomes are comparable. It can also be observed that the \(Q_{ext}^{2}\) values obtained with the fuzzy models are distributed almost symmetrically (skewness = 0.095); while the \(Q_{ext}^{2}\) values obtained with the nonfuzzy models are skewed to the right (skewness = 0.727). These results suggest that the models based on the fuzzy QuBiLSMIDAS 3DMDs tend to have a better behavior.
On the other hand, according to the results obtained from the Wilcoxon signedrank test \(\left( {p_{value} \approx 0. 0 0 8} \right)\) (Additional file 10), it can be statistically stated that the \(Q_{ext}^{2}\) values achieved by the fuzzy models differ to the ones achieved by the nonfuzzy models. In this sense, if the performances achieved by the models built on each dataset are examined (Additional file 8), it can be appreciated that for the ACHE dataset, the fuzzy model built presents the best progress of all, for a 34.58% of improvement with regard to the nonfuzzy model. Moreover, as for the THR, THERM, ACE, DHFR, COX2 and BZR datasets, the respective fuzzy models improve their predictive abilities a 13.11%, 11.99%, 8.42%, 5.65%, 5.05% and 3.41% with respect to the ones achieved by the nonfuzzy models. Only in the GPB dataset, the improvement of the fuzzy model is insignificant (0.08%). Therefore, in a general sense, it can be concluded that the Choquet integralbased fuzzy 2D/3DMDs calculation from LOVIs/LOEIs constitutes a prominent alternative to encode relevant chemical information.
Conclusions
General approaches to compute fuzzy 2D/3DMDs from the contribution of each atom (LOVIs) or bond (LOEIs) within a molecule were introduced, by using the Choquet integral as fuzzy aggregation operator. The Choquet integral is rather different from the other norm, mean and statisticbased (nonfuzzy) operators used to date. It performs a reordering step to fuse according to the magnitude of the criteria and, in addition, it considers the interrelation among criteria by using a fuzzy measure. In this work, the fuzzy L_{mδ}measure was used to compute the importance of the interrelation among atom/bond contributions (LOVIs/LOEIs). In this way, fuzzy descriptors can be derived from traditional or recent descriptors; e.g. fuzzy Balabanlike indices.
The feasibility of this proposal was assessed using the QuBiLSMIDAS descriptors, by performing modeling studies on eight chemical datasets. It was demonstrated that with the Choquet integralbased descriptors, models with better predictive power can be built, if compared to the models built with the descriptors computed from the other nonfuzzy operators. These outcomes were statistically corroborated using the Wilcoxon signedrank test. All in all, it can be concluded that the use of the Choquet integral as a fuzzy aggregation operator constitutes a prominent way to extract useful structural information of the molecules and, in this way, enhance the modeling capacity of several existing molecular descriptors in ADMETox and pharmacological endpoints.
References
Tan C (2011) Generalized intuitionistic fuzzy geometric aggregation operator and its application to multicriteria group decision making. Soft Comput 15(5):867–876
Mokarram M, Hojati M (2017) Using ordered weight averaging (OWA) aggregation for multicriteria soil fertility evaluation by GIS (case study: southeast Iran). Comput Electron Agric 132:1–13
Marrara S, Pasi G, Viviani M (2017) Aggregation operators in information retrieval. Fuzzy Set Syst 324:3–19
Calvo T et al (2002) Aggregation operators: properties, classes and construction methods. In: Calvo T, Mayor G, Mesiar R (eds) Aggregation operators: new trends and applications. PhysicaVerlag, Heidelberg, pp 3–104
Beliakov G, Pradera A, Calvo T (2007) Averaging functions. In: Aggregation functions: a guide for practitioners. Springer, Berlin, pp 39–122
Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190
Yager RR (1993) Families of OWA operators. Fuzzy Set Syst 59(2):125–148
Merigó JM, GilLafuente AM (2009) The induced generalized OWA operator. Inf Sci 179(6):729–741
Sugeno M (1974) Theory of fuzzy integrals and its applications. Tokyo Institute of Technology, Tokyo
Burkill JC (2004) The Lebesgue integral, vol 40. Cambridge University Press, Cambridge, p 108
Grabisch M, Murofushi T, Sugeno M (2000) Fuzzy measures and integrals: theory and applications. In: Michio S, Toshiaki M (eds) Studies in fuzziness and soft computing, vol 40. PhysicaVerlag, New York
Grabisch M, Labreuche C (2016) Fuzzy measures and integrals in MCDA. In: Greco S, Ehrgott M, Figueira JR (eds) Multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 553–603
Choquet G (1954) Theory of capacities. Annales de l’institut Fourier 5:131–295
Marichal JL (2000) An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria. IEEE Trans Fuzzy Syst 8(6):800–807
Marichal JL (2002) Aggregation of interacting criteria by means of the discrete Choquet integral. In: Calvo T, Mayor G, Mesiar R (eds) Aggregation operators: new trends and applications. PhysicaVerlag, Heidelberg, pp 224–244
Narukawa Y (2012) Choquet integral on the real line as a generalization of the OWA operator. In: Torra V et al (eds) Proceedings of modeling decisions for artificial intelligence: 9th international conference, MDAI 2012, Girona, Catalonia, Spain, November 21–23, 2012. Springer, Berlin, pp 56–65
Chiţescu I, Plăviţu A (2017) Computing Choquet integrals. Fuzzy Set Syst 327(Supplement C):48–68
Karczmarek P, Kiersztyn A, Pedrycz W (2017) Generalized choquet integral for face recognition. Int J Fuzzy Syst 20(3):1047–1055
Barrenechea E et al (2013) Using the Choquet integral in the fuzzy reasoning method of fuzzy rulebased classification systems. Axioms 2(2):208
Wang Z, Yang R, Leung KS (2011) Data mining with fuzzy data. In: Zadeh LA (ed) Nonlinear integrals and their applications in data mining. World Scientific, Singapore, pp 272–327
Ferreira JJM, Jalali MS, Ferreira FAF (2018) Enhancing the decisionmaking virtuous cycle of ethical banking practices using the Choquet integral. J Bus Res 88:492–497
Demirel T et al (2018) Choquet integralbased hesitant fuzzy decisionmaking to prevent soil erosion. Geoderma 313:276–289
Liu B et al (2018) An intervalvalued 2tuple linguistic group decisionmaking model based on the Choquet integral operator. Int J Inf Sci 49(2):407–424
Bajorath J (2017) Molecular similarity concepts for informatics applications. In: Keith JM (ed) Bioinformatics: volume II: structure, function, and applications. Springer, New York, pp 231–245
Masand VH et al (2017) QSAR modeling for antihuman African trypanosomiasis activity of substituted 2phenylimidazopyridines. J Mol Struct 1130:711–718
Amin SA et al (2017) An integrated multiQSAR modeling approach for designing Knoevenageltype indoles with enhancing cytotoxic profiles. Curr Comput Aided Drug Des 13(4):336–345
De P, Roy K (2018) Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors. SAR QSAR Environ Res 29(4):319–337
Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. Methods and principles in medicinal chemistry, 2nd edn. WileyVCH, Weinheim
Karelson M, Lobanov VS, Katritzky AR (1996) Quantumchemical descriptors in QSAR/QSPR studies. Chem Rev 96(3):1027–1044
Barigye SJ et al (2014) Trends in information theorybased chemical structure codification. Mol Divers 18(3):673–686
Randić M, Razinger M (1995) On characterization of molecular shapes. J Chem Inf Comput Sci 35(3):594–606
Balaban AT (1995) Local (atomic) and global (molecular) graphtheoretical descriptors. SAR QSAR Environ Res 3(2):81–95
MarreroPonce Y et al (2012) Derivatives in discrete mathematics: a novel graphtheoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application. J Comput Aided Mol Des 26(11):1229–1246
Barigye SJ et al (2013) Shannon’s, mutual, conditional and joint entropybased information indices. Generalization of global indices defined from local vertex invariants. Curr Comput Aided Drug Des 9(2):164–183
GarcíaJacas CR et al (2014) QuBiLSMIDAS: a parallel freesoftware for molecular descriptors computation based on multilinear algebraic maps. J Comput Chem 35(18):1395–1409
Cubillán N et al (2015) Novel global and local 3D atombased linear descriptors of the Minkowski distance matrix: theory, diversity–variability analysis and QSPR applications. J Math Chem 53(9):2028–2064
ValdésMartiní JR et al (2017) QuBiLSMAS, open source multiplatform software for atom and bondbased topological (2D) and chiral (2.5D) algebraic molecular descriptors computations. J Cheminform 9(1):35
GarcíaJacas CR et al (2018) GOWAWA aggregation operatorbased global molecular characterizations: weighting atom/bond contributions (LOVIs/LOEIs) according to their influence in the molecular encoding. Mol Inf. https://doi.org/10.1002/minf.201800039
MarreroPonce Y et al (2015) Optimum search strategies or novel 3D molecular descriptors: is there a stalemate? Curr Bioinform 10(5):533–564
GarcíaJacas CR et al (2014) Nlinear algebraic maps to codify chemical structures: is a suitable generalization to the atompairs approaches? Curr Drug Metab 15(4):441–469
Martínez Santiago O et al (2015) Extending graph (discrete) derivative descriptors to ntuple atomrelations. MATCH Commun Math Comput Chem 73(2):397–420
MartínezSantiago O et al (2017) Exploring the QSAR’s predictive truthfulness of the novel Ntuple discrete derivative indices on benchmark datasets. SAR QSAR Environ Res 28(5):367–389
GarcíaJacas CR et al (2016) Examining the predictive accuracy of the novel 3D Nlinear algebraic molecular codifications on benchmark datasets. J Cheminform 8(10):1–16
Medina Marrero R et al (2015) QuBiLsMAS method in early drug discovery and rational drug identification of antifungal agents. SAR QSAR Environ Res 26(11):943–958
MenesesMarcel A et al (2018) Drug repositioning for novel antitrichomonas from known antiprotozoan drugs using hierarchical screening. Future Med Chem 10(8):863–878
GarcíaJacas CR et al (2017) Conformationdependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators. SAR QSAR Environ Res 28(1):41–58
Horvath D, Mao B (2003) Neighborhood behavior. Fuzzy molecular descriptors and their influence on the relationship between structural similarity and property similarity. QSAR Comb Sci 22(5):498–509
Bonachéra F et al (2006) Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted molecular similarity scoring schemes. J Chem Inf Model 46(6):2457–2477
Bonachéra F, Horvath D (2008) Fuzzy tricentric pharmacophore fingerprints. 2. Application of topological fuzzy pharmacophore triplets in quantitative structure–activity relationships. J Chem Inf Model 48(2):409–425
Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Set Syst 100(Supplement 1):9–34
Shapley LS (1953) A value for nperson games. In: Kuhn HW, Tucker AW (eds) Contributions to the theory of games. Princeton University Press, Princeton, pp 307–317
Grabisch M (1997) korder additive discrete fuzzy measures and their representation. Fuzzy Set Syst 92(2):167–189
Mohammed MA (2003) Qmeasures: an efficientextension of the Sugenomeasure. IEEE Trans Fuzzy Syst 11(3):419–426
Liu HC et al (2010) Composed fuzzy measure of maximized Lmeasure and deltameasure. WSEAS Trans Inf Sci Appl 7(4):474–483
Ohlan A (2016) Overview on development of fuzzy information measures. IJARESM 4(12):17–22
Liu HC et al (2007) A novel fuzzy measure and its choquet integral regression model. In: 2007 International conference on machine learning and cybernetics. IEEE, Hong Kong, China
Liu HC (2009) Maximized Lmeasure and its Choquet integral regression model. In: 2009 10th international symposium on pervasive systems, algorithms, and networks. IEEE, Kaohsiung, Taiwan
Liu HC et al (2009) Theory of multivalent deltafuzzy measures and its application. WSEAS Trans Inf Sci Appl 6(6):1061–1070
Mohd WRW, Abdullah L (2017) Choquet integral with respect to maximized Lmeasure and deltameasure. AIP conference proceedings, vol 1870, no 1
Höhle U (1982) Integration with respect to fuzzy measures. In: Proceedings of IFAC symposium on theory and applications of digital control. New Delhi
Murofushi T, Sugeno M (1989) An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Set Syst 29(2):201–227
Murofushi T, Sugeno M (1991) A theory of fuzzy measures: representations, the Choquet integral, and null sets. J Math Anal Appl 159(2):532–549
Tan C, Chen X (2010) Induced choquet ordered averaging operator and its application to group decision making. Int J Intell Syst 25(1):59–82
Llamazares B (2015) Constructing Choquet integralbased operators that generalize weighted means and OWA operators. Inf Fusion 23(Supplement C):131–138
FathTabar G (2011) Old and new Zagreb indices of graphs. MATCH Commun Math Comput Chem 65(1):79–84
Deng H (2011) On the sumBalaban index. MATCH Commun Math Comput Chem 66(1):273–284
Diudea MV, Gutman I (1998) Wienertype topological indices. Croat Chem Acta 71(1):21–51
Randic M (1975) Characterization of molecular branching. J Am Chem Soc 97(23):6609–6615
Kier LB, Hall LH (1991) A differential molecular connectivity index. Mol Inform 10(2):134–140
GarcíaJacas CR et al (2017) Tensor algebrabased geometric methodology to codify central chirality on organic molecules. SAR QSAR Environ Res 28(6):541–556
GarcíaJacas CR et al (2016) Ntuple topological/geometric cutoffs for 3D Nlinear algebraic molecular codifications: variability, linear independence and QSAR analysis. SAR QSAR Environ Res 27(12):949–975
Sinkhorn R, Knopp P (1967) Concerning nonnegative matrices and doubly stochastic matrices. Pac J Math 21(2):343–348
Xu Z (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20(8):843–865
GarcíaJacas CR et al (2015) Multiserver approach for highthroughput molecular descriptors calculation based on multilinear algebraic maps. Mol Inform 34(1):60–69
Todeschini R et al (2003) MobyDigs: software for regression and classification models by genetic algorithms. In: Leardi R (ed) Natureinspired methods in chemometrics: genetic algorithms and artificial neural networks. Amsterdam, Elsevier, pp 141–167
Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110(18):5959–5967
Schnitker J, Gopalaswamy R, Crippen GM (1997) Objective models for steroid binding sites of human globulins. J Comput Aided Mol Des 11(1):93–110
Palyulin VA, Radchenko EV, Zefirov NS (2000) Molecular field topology analysis method in QSAR studies of organic compounds. J Chem Inf Comput Sci 40(3):659–667
Tominaga Y, Fujiwara I (1997) Predictionweighted partial leastsquares regression method (PWPLS) 2: application to CoMFA. J Chem Inf Comput Sci 37(6):1152–1157
Pastor M et al (2000) GRidINdependent Descriptors (GRIND): a novel class of alignmentindependent threedimensional molecular descriptors. J Med Chem 43(17):3233–3243
Westphal U (1983) Corticosteroidbinding globulin. Mol Cell Biochem 55(2):145–157
Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92
Sutherland JJ, O’Brien LA, Weaver DF (2004) A comparison of methods for modeling quantitative structureactivity relationships. J Med Chem 47(22):5541–5554
Klamt A et al (2012) COSMOsar3D: molecular field analysis based on local COSMO σprofiles. J Chem Inf Model 52(8):2157–2164
Hinselmann G et al (2011) jCompoundMapper: an open source Java library and commandline tool for chemical fingerprints. J Cheminform 3(1):3
Bruce CL et al (2007) Contemporary QSAR classifiers compared. J Chem Inf Model 47(1):219–227
Godden JW, Stahura FL, Bajorath J (2000) Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations. J Chem Inf Comput Sci 40(3):796–800
Urias RWP et al (2015) IMMAN: free software for information theorybased chemometric analysis. Mol Divers 19(2):305–319
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83
AlcaláFdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318
Authors’ contributions
CRGJ, YMP and FCG proposed the theory of the Choquet integralbased QuBiLSMIDAS 3DMDs, supervised the QSAR modeling, the design of the GUI and prepared the manuscript. LCL and JSL implemented the GUI designed and performed the QSAR modeling. JSL worked in the revision of the manuscript. MPM and RVY performed the statistical assessment. All authors read and approved the final manuscript.
Acknowledgements
CRGJ acknowledges the support from “Dirección General de Asuntos del Personal Académico” (DGAPA) for the postdoctoral fellowship at “Instituto de Química, Universidad Nacional Autónoma de México (UNAM)” in 2016–2018. YMP acknowledges the support from USFQ “Chancellor Grant 2016 (Project ID5454)”.
Competing interests
The authors declare no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author information
Authors and Affiliations
Corresponding author
Additional files
Additional file 1.
Definition of the Sugeno Fuzzy λmeasure and the Fuzzy Pmeasure.
Additional file 2.
Steroid dataset and projects used to determine the best configurations for the computation of fuzzy densities.
Additional file 3.
Matrices of descriptors obtained from the calculation of the projects created in Additional file 2 on the Steroid dataset.
Additional file 4.
Statistical parameters for the models built on each descriptors matrix represented in Additional file 3.
Additional file 5.
Ranking of the configurations for the computation of fuzzy densities according to the results represented in Additional file 4.
Additional file 6.
Chemical datasets and projects to assess the performance between fuzzy and nonfuzzy QuBiLSMIDAS descriptors.
Additional file 7.
Descriptors included in the best models for 3, 5 and 7 variables built on each chemical dataset shown in Additional file 6.
Additional file 8.
Best nonfuzzy and fuzzy models of 7 variables built on each chemical dataset shown in Additional file 6.
Additional file 9.
Descriptive statistics for the external predictions achieved by the best nonfuzzy and fuzzy models of 7 variables represented in Additional file 8.
Additional file 10.
Results of the Wilcoxon test.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
GarcíaJacas, C.R., CabreraLeyva, L., MarreroPonce, Y. et al. Choquet integralbased fuzzy molecular characterizations: when global definitions are computed from the dependency among atom/bond contributions (LOVIs/LOEIs). J Cheminform 10, 51 (2018). https://doi.org/10.1186/s1332101803067
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1332101803067