From Knowledge Generation to Knowledge Archive. A General Strategy Using TOPS-MODE with DEREK To Formulate New Alerts for Skin Sensitization

Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles

Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure-activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.

Potential implications of vouacapan compounds for insecticidal activity: an in silico study

BACKGROUND

From the fruits and seeds of the species of Pterodon, it is possible to obtain two main products: the essential oil and oleoresin. In oleoresin, numerous vouacapan compounds have been demonstrated to have biological potential, including insecticidal activity.

OBJECTIVE

In silico studies were performed to identify potential candidates for natural insecticides among the vouacapans present in the genus Pterodon.

MATERIALS AND METHODS

Molecular docking and molecular dynamics studies were performed to analyze the interaction of vouacapan compounds with acetylcholinesterase of Drosophila melanogaster. Pharmacokinetic parameters regarding physicochemical properties, plasma protein binding, and activity in the central nervous system were evaluated. The toxicological properties of the selected molecules were predicted using Malathion as the reference compound.

RESULTS

6α,7β-dimethoxivouacapan-17-ene (15) showed a high number of interactions and scores in molecular docking studies. This result suggests that this compound exhibits an inhibitory activity of the enzyme acetylcholinesterase. Regarding physicochemical properties, this compound showed the best results, besides presenting low cutaneous permeability values, suggesting null absorption. Molecular dynamics studies demonstrated few conformational changes in the structure of the complex formed by compound 4 and acetylcholinesterase enzyme throughout the simulation time.

CONCLUSION

It was determined that compound 4 (vouacapan 6α,7β,17β,19-tetraol) is an excellent candidate for usage as a natural insecticide.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Predicting pungency and understanding the pungency mechanism of capsaicinoids using TOPS-MODE approach

Quantitative structure-property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training set. The predictive power of the model was validated through a test set and several external validation parameters. This showed that the TOPS-MODE descriptors weighted by bond dipole moments, van der Waals atomic radii, and the total solute hydrogen bond basicity affected pungency. The contributions of certain bonds and fragments to pungency were used to understand the pungency mechanism of capsaicinoids. The selected model can more accurately predict pungency of capsaicinoids compared than those found in the literature, and especially bring insights into the structural features and chemical factors related to pungency.

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties

Background

Advanced structure-activity relationship (SAR) modeling can be used as an alternative tool for identification of skin sensitizers and in improvement of the medical diagnosis and more effective practical measures to reduce the causative chemical exposures. It can also circumvent ethical concern of using animals in toxicological tests, and reduce time and cost. Compounds with aniline or phenol moieties represent two large classes of frequently skin sensitizing chemicals but exhibiting very variable, and difficult to predict, potency. The mechanisms of action are not well-understood.

Methods

A group of mechanistically hard-to-be-classified aniline and phenol chemicals were collected. An in silico model was established by statistical analysis of quantum descriptors for the determination of the relationship between their chemical structures and skin sensitization potential. The sensitization mechanisms were investigated based on the features of the established model. Then the model was utilized to analyze a subset of FDA approved drugs containing aniline and/or phenol groups for prediction of their skin sensitization potential.

Results and discussion

A linear discriminant model using the energy of the highest occupied molecular orbital (ϵ_HOMO) as the descriptor yielded high prediction accuracy. The contribution of ϵ_HOMO as a major determinant may suggest that autoxidation or free radical binding could be involved. The model was further applied to predict allergic potential of a subset of FDA approved drugs containing aniline and/or phenol moiety. The predictions imply that similar mechanisms (autoxidation or free radical binding) may also play a role in the skin sensitization caused by these drugs.

Conclusions

An accurate and simple quantum mechanistic model has been developed to predict the skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol chemicals. The model could be useful for the skin sensitization potential predictions of a subset of FDA approved drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/2050-6511-15-76) contains supplementary material, which is available to authorized users.

Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

QSAR study of liver specificity of carcinogenicity of N-nitroso compounds

The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.

Predicting carcinogenicity and understanding the carcinogenic mechanism of N-nitroso compounds using a TOPS-MODE approach

A linear discriminant analysis (LDA) coupled with an enhanced replacement method (ERM) was used as an alternative method to predict the carcinogenicity of N-nitroso compounds (NOCs) in rats. This presented LDA based on the topological substructural molecular descriptors (TOPS-MODE) approach was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 111 NOCs with a good classification value of 90.1%. The predictive power of the LDA model was validated through an external validation set (37 compounds) with a prediction accuracy of 94.6% and a leave-one-out cross-validation procedure (LOOCV) with a good prediction of 86.5%. This methodology showed that the TOPS-MODE descriptors weighted, respectively, by bond dipole moment and Abraham solute descriptor dipolarity/polarizability affected the NOC carcinogenicity. The contributions of certain bonds and fragments to carcinogenicity were used to assess biotransformation and carcinogenic mechanisms. The positive contribution of the carbon-nitrogen single bond (between the N-nitroso group and α-carbon to the N-nitroso group) indicated that the α-hydroxylation reaction could occur at the α-carbon or otherwise not occur. Similarly, the contributions from the molecular fragment could be applied to indicate whether the fragments generated an alkylating agent. These results suggested that this approach could discriminate between carcinogenic and noncarcinogenic NOCs, thereby providing insight into the structural features and chemical factors related to NOC carcinogenicity.

Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods

Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was performed to develop global predictive classification models that are applicable to whole chemical space. With this aim, we employed two automated procedures, (a) D-optimal design to select optimal members of the training and test sets and (b) k-Nearest Neighbour classification (kNN) method along with Genetic Algorithms (GA-kNN Classification) to select significant and independent descriptors in order to build the models. This methodology helped us to derive multiple models, M1-M5, that are stable and robust. The best among them, model M1 (CCR(train) = 84.3%, CCR(test) = 87.2% and CCR(ext) = 80.4%), is based on six neighbours and nine descriptors and further suggests that: (a) it is stable and robust and performs better than the reported models in literature, and (b) the combination of D-optimal design and GA-kNN classification approach is a very promising approach. Consensus prediction based on the models M1-M5 improved the CCR of training, test and external validation datasets by 3.8%, 4.45% and 3.85%, respectively, over M1. From the analysis of the physical meaning of the selected descriptors, it is inferred that the skin sensitization potential of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (i) lipophilicity, (ii) atomic polarizability, (iii) shape, (iii) electrostatic interactions, and (iv) chemical reactivity.

A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds

Chemically reactive, alpha, beta-unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity. This toxic property can often be related to chemical structure, in particular to specific molecular substructures or fragments (alerts), which can then be used in specialized software or expert systems for predictive purposes. In the past, there have been many attempts to predict the mutagenicity of alpha, beta-unsaturated carbonyl compounds through quantitative structure activity relationships (QSAR) but considering only one exclusive endpoint: the Ames test. Besides, even though those studies give a comprehensive understanding of the phenomenon, they do not provide substructural information that could be useful forward improving expert systems based on structural alerts (SAs). This work reports an evaluation of classification models to probe the mutagenic activity of alpha, beta-unsaturated carbonyl compounds over two endpoints--the Ames and mammalian cell gene mutation tests--based on linear discriminant analysis along with the topological Substructure molecular design (TOPS-MODE) approach. The obtained results showed the better ability of the TOPS-MODE approach in flagging structural alerts for the mutagenicity of these compounds compared to the expert system TOXTREE. Thus, the application of the present QSAR models can aid toxicologists in risk assessment and in prioritizing testing, as well as in the improvement of expert systems, such as the TOXTREE software, where SAs are implemented.

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity end-points associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Allergic contact dermatitis--formation, structural requirements, and reactivity of skin sensitizers

Contact allergy is caused by a wide range of chemicals after skin contact. Its clinical manifestation, allergic contact dermatitis (ACD), is developed upon repeated contact with the allergen. This perspective focuses on two areas that have yielded new useful information during the last 20 years: (i) structure-activity relationship (SAR) studies of contact allergy based on the concept of hapten-protein binding and (ii) mechanistic investigations regarding activation of nonsensitizing compounds to contact allergens by air oxidation or skin metabolism. The second area is more thoroughly reviewed since the full picture has previously not been published. Prediction of the sensitizing capacity of a chemical is important to avoid outbreaks of ACD in the population. Much research has been devoted to the development of in vitro and in silico predictive testing methods. Today, no method exists that is sensitive enough to detect weak allergens and that is robust enough to be used for routine screening. To cause sensitization, a chemical must bind to macromolecules (proteins) in the skin. Expert systems containing information about the relationship between the chemical structure and the ability of chemicals to haptenate proteins are available. However, few designed SAR studies based on mechanistic investigations of prohaptens have been published. Many compounds are not allergenic themselves but are activated in the skin (e.g., metabolically) or before skin contact (e.g., via air oxidation) to form skin sensitizers. Thus, more basic research is needed on the chemical reactions involved in the antigen formation and the immunological mechanisms. The clinical importance of air oxidation to activate nonallergenic compounds has been demonstrated. Oxidized fragrance terpenes, in contrast to the pure terpenes, gave positive patch test reactions in consecutive dermatitis patients as frequently as the most common standard allergens. This shows the importance of using compounds to which people are exposed when screening for ACD in dermatology clinics.

Quantitative structure-antifungal activity relationships of some benzohydrazides against Botrytis cinerea

Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds

Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here - Quantitative Structure-Activity Relationship (QSAR) - are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions -- expressed as fragment contributions to the carcinogenic activity -- that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.

Global (Q)SARs for skin sensitisation: assessment against OECD principles

As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that "statistical" (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary.

Quantitative structure–activity relationship studies for the prediction of antifungal activity of N-arylbenzenesulfonamides against Botrytis cinerea

The Botrytis cinerea is one of the most interesting fungal pathogens. It can infect almost every plant and plant part and cause early latent infections which damage the fruit before ripening. The QSAR is an alternative method for the research of new and better fungicides against B. cinerea. This paper describes the results of applying a topological sub-structural molecular design (TOPS-MODE) approach for predicting the antifungal activity of 28 N-arylbenzenesulfonamides. The model described 86.1% of the experimental variance, with a standard deviation of 0.223. Leave-one-out and leave-group-out cross validation was carried out with the aim of evaluating the predictive power of the model. The values of their respective squared correlations coefficients were 0.754 and 0.741. The TOPS-MODE approach was compared with three other predictive models, but none of these could explain more than 72.8% of the variance with the same number of variables. In addition, this approach enabled the assessment of the contribution of different bonds to antifungal activity, thereby making the relationships between structure and biological activity more transparent. It was found that the fungicidal activity of the chemicals analyzed was increased by the presence of a sulfonamide group bonded to two aromatics rings, making this group the most important of the molecule. The majority of the substituents present in the aromatic rings have an electron withdrawing effect and they contribute to a smaller degree than the sulfonamide group to the property under study. The aromatic moiety plays an important role in this activity; its contribution changes with different substituents. Generally, the nitro group has a positive and great contribution to the biological property but when this group is involved in some compounds in ortho effect with the SO2 moiety of the sulfonamide group a lower value of contribution is observed for both groups.

Automatic extraction of structural alerts for predicting chromosome aberrations of organic compounds

We use the topological sub-structural molecular design (TOPS-MODE) approach to formulate structural alert rules for chromosome aberration (CA) of organic compounds. First, a classification model was developed to group chemicals as active/inactive respect to CA. A procedure for extracting structural information from orthogonalized TOPS-MODE descriptors was then implemented. The contributions of bonds to CA in all the molecules studied were then generated using the orthogonalized classification model. Using this information we propose 22 structural alert rules which are ready to be implemented in expert systems for the automatic prediction of CA. They include, among others, structural alerts for N-nitroso compounds (ureas, urethanes, guanidines, triazines), nitro compounds (aromatic and heteroaromatic), alkyl esters or phosphoric acids, alkyl methanesulfonates, sulphonic acids and sulphonamides, epoxides, aromatic amines, azaphenanthrene hydrocarbons, etc. The chemico-biological analysis of some of the structural alerts found is also carried out showing the potential of TOPS-MODE as a knowledge generator.

Classification study of skin sensitizers based on support vector machine and linear discriminant analysis

The support vector machine (SVM), recently developed from machine learning community, was used to develop a nonlinear binary classification model of skin sensitization for a diverse set of 131 organic compounds. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a diverse set of molecular descriptors calculated from molecular structures alone. These six descriptors could reflect the mechanic relevance to skin sensitization and were used as inputs of the SVM model. The nonlinear model developed from SVM algorithm outperformed LDA, which indicated that SVM model was more reliable in the recognition of skin sensitizers. The proposed method is very useful for the classification of skin sensitizers, and can also be extended in other QSAR investigation.

A topological substructural approach for the prediction of P-glycoprotein substrates

A topological substructural molecular design approach (TOPS-MODE) has been used to predict whether a given compound is a P-glycoprotein (P-gp) substrate or not. A linear discriminant model was developed to classify a data set of 163 compounds as substrates or nonsubstrates (91 substrates and 72 nonsubstrates). The final model fit the data with sensitivity of 82.42% and specificity of 79.17%, for a final accuracy of 80.98%. The model was validated through the use of an external validation set (40 compounds, 22 substrates and 18 nonsubstrates) with a 77.50% of prediction accuracy; fivefold full cross-validation (removing 40 compounds in each cycle, 80.50% of good prediction) and the prediction of an external test set of marketed drugs (35 compounds, 71.43% of good prediction). This methodology evidenced that the standard bond distance, the polarizability and the Gasteiger-Marsilli atomic charge affect the interaction with the P-gp; suggesting the capacity of the TOPS-MODE descriptors to estimate the P-gp substrates for new drug candidates. The potentiality of the TOPS-MODE approach was assessed with a family of compounds not covered by the original training set (6-fluoroquinolones), and the final prediction had a 77.7% of accuracy. Finally, the positive and negative substructural contributions to the classification of 6-fluoroquinolones, as P-gp substrates, were identified; evidencing the possibilities of the present approach in the lead generation and optimization processes.