Chemoinformatics in Multi-target Drug Discovery for Anti-cancer Therapy: In Silico Design of Potent and Versatile Anti-brain Tumor Agents (original) (raw)
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European Journal of Pharmaceutical Sciences, 2012
The discovery of new and more efficient anti-cancer chemotherapies is a field of research in expansion and growth. Breast cancer (BC) is one of the most studied cancers because it is the principal cause of cancer deaths in women. In the active area for the search of more potent anti-BC drugs, the use of approaches based on Chemoinformatics has played a very important role. However, until now there is no methodology able to predict anti-BC activity of compounds against more than one BC cell line, which should constitute a greater interest. In this study we introduce the first chemoinformatic multi-target (mt) approach for the in silico design and virtual screening of anti-BC agents against 13 cell lines. Here, an mt-QSAR discriminant model was developed using a large and heterogeneous database of compounds. The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set. The validation of the model was carried out by using a prediction set which showed 89.79% of correct classification for active and 92.49% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BC activity were calculated. Several fragments were identified as potential substructural features responsible for anti-BC activity and new molecules designed from those fragments with positive contributions were suggested as possible potent and versatile anti-BC agents.
Bioorganic & Medicinal Chemistry, 2012
The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents.
Journal of Computer-Aided Molecular Design, 2007
A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC 50 using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor (kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R 2 (q 2) values were greater than 0.5 for the training sets and the correlation coefficient R 2 values were greater than 0.6 for the test sets. Furthermore, the q 2 values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC 50 of 1.8µM implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient (R 2) between the experimental and predicted EC 50 for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds.
BMC cancer, 2015
In past, numerous quantitative structure-activity relationship (QSAR) based models have been developed for predicting anticancer activity for a specific class of molecules against different cancer drug targets. In contrast, limited attempt have been made to predict the anticancer activity of a diverse class of chemicals against a wide variety of cancer cell lines. In this study, we described a hybrid method developed on thousands of anticancer and non-anticancer molecules tested against National Cancer Institute (NCI) 60 cancer cell lines. Our analysis of anticancer molecules revealed that majority of anticancer molecules contains 18-24 carbon atoms and are dominated by functional groups like R2NH, R3N, ROH, RCOR, and ROR. It was also observed that certain substructures (e.g., 1-methoxy-4-methylbenzene, 1-methoxy benzene, Nitrobenzene, Indole, Propenyl benzene) are more abundant in anticancer molecules. Next, we developed anticancer molecule prediction models using various machine-l...
Journal of Molecular Modeling, 2003
A simple stochastic approach, designed to model the movement of electrons throughout chemical bonds, is introduced. This model makes use of a Markov matrix to codify useful structural information in QSAR. The self-return probabilities of this matrix throughout time (SRπk ) are then used as molecular descriptors. Firstly, a calculation of SRπk is made for a large series of anticancer and non-anticancer chemicals. Then, k-Means Cluster Analysis allows us to split the data series into clusters and ensure a representative design of training and predicting series. Next, we develop a classification function through Linear Discriminant Analysis (LDA). This QSAR discriminates between anticancer compounds and non-active compounds with a correct global classification of 90.5% in the training series. The model also correctly classified 86.07% of the compounds in the predicting series. This classification function is then used to perform a virtual screening of a combinatorial library of coumarins. In this connection, the biological assay of some furocoumarins, selected by virtual screening using the present model, gives good results. In particular, a tetracyclic derivative of 5-methoxypsoralen (5-MOP) has an IC50 against HL-60 tumoral line around 6 to 10 times lower than those for 8-MOP and 5-MOP (reference drugs), respectively. Finally, application of Iso-contribution Zone Analysis (IZA) provides structural interpretation of the biological activity predicted with this QSAR. Figure IZA of compound 1
SAR and QSAR in Environmental Research, 2020
Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
ACS Omega, 2019
Pancreatic cancers are widely recognized as a group of neoplasms with one of the poorest prognoses in oncology research. Despite the advances achieved in drug design and development, there is no effective cure for pancreatic cancers, and the current chemotherapeutic regimens increase the survival rate by only a few months. As an integral part of all modern drug discovery campaigns, computer-aided approaches can represent a promising alternative change to accelerate the early discovery of potent anti-pancreatic cancer agents. To date, however, most of the efforts made so far have focused on small series of structurally related chemicals, where the anti-pancreatic cancer activity has been measured against only one cancer cell line. In addition, no rational insight has been provided in the sense of unveiling the physicochemical aspects and the structural features that the molecules should possess to increase the anti-pancreatic cancer activity. This work reports the first multicellular target QSAR model based on ensemble learning (mct-QSAR-EL) that allows the simultaneous prediction and design of molecules with activity against different pancreatic cancer cell lines, which exhibit different degrees of sensitivity to chemical treatment. The mct-QSAR-EL model displayed sensitivities and specificities higher than 80% in both training and test sets. The physicochemical and structural interpretations of the molecular descriptors in the model permitted the selection of several fragments with potentially positive contributions to the increase of the anti-pancreatic cancer activity. These fragments were then assembled to design new molecules. The designed molecules were predicted as multicell line inhibitors by the mct-QSAR-EL model, and these results converged with the predictions performed by recently reported models. The designed molecules complied with Lipinski's rule of five and its variants.
Chemoinformatics and drug discovery
Molecules, 2002
This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.
Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models
Organic and Medicinal Chemistry Letters, 2011
Background QSAR is among the most extensively used computational methodology for analogue-based design. The application of various descriptor classes like quantum chemical, molecular mechanics, conceptual density functional theory (DFT)- and docking-based descriptors for predicting anti-cancer activity is well known. Although in vitro assay for anti-cancer activity is available against many different cell lines, most of the computational studies are carried out targeting insufficient number of cell lines. Hence, statistically robust and extensive QSAR studies against 29 different cancer cell lines and its comparative account, has been carried out. Results The predictive models were built for 266 compounds with experimental data against 29 different cancer cell lines, employing independent and least number of descriptors. Robust statistical analysis shows a high correlation, cross-validation coefficient values, and provides a range of QSAR equations. Comparative performance of each c...
Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads
European Journal of Medicinal Chemistry, 2011
A sarcoma is a type of cancer which is originated from the connective tissue cells. With the time, several sarcomas have become resistant to the current anti-tumoral drugs. Many works have been reported in order to explain some mechanisms of resistance in different types of sarcomas and around 2000 compounds have been tested as anti-sarcoma agents against several sarcoma cell lines. However, there is no an available methodology for the rational design of compounds with anti-sarcoma activity. The present work develops a unified fragment-based approach by employing a multi-target QSAR model for the efficient search and design of new anti-sarcoma agents against 12 sarcoma cell lines. The model was obtained with the use of a heterogeneous database of compounds and it was based on substructural descriptors. The percentages of correct classification of active and inactive compounds were higher than 85% in both cases. Also, the present approach provided the rapid extraction of substructural alerts responsible of anti-sarcoma profile by calculating the quantitative contributions of fragments to antisarcoma activity. To our knowledge, this is the first attempt to calculate the probabilities of antisarcoma activity of compounds against several sarcoma cell lines simultaneously, using a unified fragment-based QSAR model.