Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds

Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activity via experimental approaches. To partially address this issue, several computational advances have been proposed. In this study, we present iACP-GCR, a model based on multitask learning on graph convolutional residual neural networks with two types of shortcut connections, to identify multitarget anticancer compounds. In our architecture, the graph convolutional residual neural networks are shared by all the prediction tasks before being separately customized. The NCI-60 data set, one of the most reliable and well-known sources of experimentally verified compounds, was used to develop our model. From that data set, we collected and refined data about compounds screened across nine cancer types (panels), including breast, central nervous system, colon, leukemia, nonsmall cell lung, melanoma, ovarian, prostate, and renal, for model training and evaluation. The model performance evaluated on an independent test set shows that iACP-GCR surpasses the three advanced computational methods for multitask learning. The integration of two shortcut connection types in the shared networks also improves the prediction efficiency. We also deployed the model as a public web server to assist the research community in screening potential anticancer compounds.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10^-4, C = 0.84, logrank-p = 3.8x10^-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10⁻⁴ (C = 0.88, logrank-p = 1.44x10^-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors

The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance-based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross-validation and independent validation datasets. It was observed that the random forest-based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open-source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti-TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants.

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure–activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson’s correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at http://biosig.unimelb.edu.au/pdcsm_cancer.

An overview of therapeutic anticancer drug monitoring based on surface enhanced (resonance) Raman spectroscopy (SE(R)RS)

Therapeutic drug monitoring (TDM) is important for many therapeutic regimens and has particular relevance for anticancer drugs which often have serious effects and whose optimum dosage can vary significantly between different patients. Many of the features of surface enhanced (resonance) Raman spectroscopy (SE(R)RS) suggest it should be very suitable for TDM of anticancer drugs and some initial studies which explore the potential of SE(R)RS for TDM of anticancer drugs have been published. This review brings this work together in an attempt to draw some general observations about key aspects of the approach, including the nature of the substrate used, matrix interference effects and factors governing adsorption of the target molecules onto the enhancing surface. There is now sufficient evidence to suggest that none of these pose real difficulties in the context of TDM. However, some issues, particularly the need to carry out multiplex measurements for TDM of combination therapies, have yet to be addressed.

Synthesis and Cytotoxic Activity of Several Novel N-Alkyl-Plinabulin Derivatives With Aryl Group Moieties

Seven novel N-alkyl-plinabulin derivatives with aryl groups moieties (nitroquinoline, 1,4-dihydroquinoline, 4-methoxybenzene, and 4-chlorobenzene) have been synthesized via aldol condensation and alkylation in one-pot, and tested for their cytotoxicity against 4 cancer cell lines (KB, HepG2, Lu, and MCF7). Compounds ( Z)−3-((6,8-dimethyl-4-oxo-1,4-dihydroquinolin-2-yl)methylene)−6-(( Z)−4-methoxybenzylidene)−1-(prop-2-yn-1-yl)piperazine-2,5-dione (5a), ( Z)−6-(( Z)−4-methoxybenzylidene)−1-(prop-2-yn-1-yl)−3-((1,6,8-trimethyl-4-oxo-1,4-dihydroquinolin-2-yl)methylene)piperazine-2,5-dione (5b), and ( Z)−3-(( Z)−4-chlorobenzylidene)−1,4-dimethyl-6-((8-methyl-4-nitroquinolin-2-yl)methylene)piperazine-2,5-dione (8) showed strong cytotoxicity against 3 of the cancer cells lines (KB, HepG2 and Lu) with IC50 values ranging from 3.04 to 10.62 µM. The quinoline-derived compounds had higher cytotoxic activity than the benzaldehyde derivatives. The successful synthesis of these derivatives offers useful information for the development of more potent vascular disrupting agents based on plinabulin.

Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D₂ modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([³H]-NPA) at D₂ receptors (D₂R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [³H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D₂R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.

Barbiturate derivatives for managing multifaceted oncogenic pathways: A mini review

Development and progression of metastasis comprises synchronized erroneous expressions of several composite pathways, which are difficult to manage simultaneously with the representative anticancer molecules. The emergence of the drug resistance and the complex interplay between these pathways further potentiates cancer related complexities. Barbiturates and their derivatives present a commendable anticancer profile by attenuating the cancer manifesting metabolic and enzymatic pathways including, but not limited to matrix metalloproteinases, xanthine oxidase, amino peptidases, histone deacetylases, and Ras/mitogen-activated protein kinase. The derivatization and conjugation of barbiturates with pharmacophores delivers a suitable hybrid profile in containing the anomalous expression of these pathways. The present report presents a succinct collation of the barbiturates and their derivatives in managing the various cancer causing pathways.

AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes

AlgPred 2.0 is a web server developed for predicting allergenic proteins and allergenic regions in a protein. It is an updated version of AlgPred developed in 2006. The dataset used for training, testing and validation consists of 10 075 allergens and 10 075 non-allergens. In addition, 10 451 experimentally validated immunoglobulin E (IgE) epitopes were used to identify antigenic regions in a protein. All models were trained on 80% of data called training dataset, and the performance of models was evaluated using 5-fold cross-validation technique. The performance of the final model trained on the training dataset was evaluated on 20% of data called validation dataset; no two proteins in any two sets have more than 40% similarity. First, a Basic Local Alignment Search Tool (BLAST) search has been performed against the dataset, and allergens were predicted based on the level of similarity with known allergens. Second, IgE epitopes obtained from the IEDB database were searched in the dataset to predict allergens based on their presence in a protein. Third, motif-based approaches like multiple EM for motif elicitation/motif alignment and search tool have been used to predict allergens. Fourth, allergen prediction models have been developed using a wide range of machine learning techniques. Finally, the ensemble approach has been used for predicting allergenic protein by combining prediction scores of different approaches. Our best model achieved maximum performance in terms of area under receiver operating characteristic curve 0.98 with Matthew's correlation coefficient 0.85 on the validation dataset. A web server AlgPred 2.0 has been developed that allows the prediction of allergens, mapping of IgE epitope, motif search and BLAST search (https://webs.iiitd.edu.in/raghava/algpred2/).

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.

Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure-activity patterns in NCI-60 cell lines

The NCI-60 cancer cell line screening panel has provided insights for development of subtype-specific chemical therapies and repurposing. By extracting chemical structure and cytotoxicity patterns, virtual screening potentially complements the availability of high-throughput assay platforms and improves bioactive compound discovery rates by computational prefiltering of candidate compound libraries. Many groups report high prediction performances in computational models of NCI-60 data when using cross-validation or similar techniques, yet prospective therapy development in novel cancers may have little to no such data and further may not have the resources to perform hit identification using large compound libraries. In contrast to bulk screening and analysis, the active learning methodology has demonstrated how to identify compounds for screening in small batches and update computational models iteratively, leading to predictive models with a minimum number of compounds, and importantly clarifying data volumes at which limits in predictive ability are achieved. Here, in replicate per-cell line experiments using 50% of data (∼20 000 compounds) as the external prediction target, predictive limits are reproducibly demonstrated at the stage of systematic selection of 10-30% of the incorporable half. The pattern was consistent across all 60 cell lines. Limits of predictability are found to be correlated to the doubling times of cell lines and the number of cellular response discontinuities (activity cliffs) present per cell line. Organization into chemical scaffolds delineated degrees of predictive challenge. These results provide key insights for strategies in developing new inhibitors in existing cell lines or for future automated therapy selection in personalized oncotherapy.

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value ~10-8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value ~10-6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value ~10-9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-value ~10-15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

Consistent Cell-selective Analog Series as Constellation Luminaries in Chemical Space

High-throughput screening data of compounds consistently tested against the same panel of cell lines is a rich source of information for interrogating cell-selectivity of a compound. Nevertheless, there is a high risk of false positives for these rapid-testing strategies. Then, a single cell-inactive compound can be mistakenly labeled as highly cell-selective if a false positive occurs in any of the cell assays. More interesting would be the case of a series of analogs, which are structurally related compounds, that have a trend to be active only against a small number of cells. To this end, it is herein proposed a proof-of-concept of a method for finding consistent cell-selective analog series of chemical compounds through analysis of high-throughput cell-compound screening data systematically obtained. Furthermore, statistics for quantifying cell-promiscuity and consistency of an analog series are presented.

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).

Effects of green synthesised silver nanoparticles (ST06-AgNPs) using curcumin derivative (ST06) on human cervical cancer cells (HeLa) in vitro and EAC tumor bearing mice models

Background

In recent years, green synthesized silver nanoparticles have been increasingly investigated for their anti-cancer potential. In the present study, we aimed at the biosynthesis of silver nanoparticles (AgNPs) using a curcumin derivative, ST06. Although, the individual efficacies of silver nanoparticles or curcumin derivatives have been studied previously, the synergistic cytotoxic effects of curcumin derivative and silver nanoparticles in a single nanoparticulate formulation have not been studied earlier specifically on animal models. This makes this study novel compared to the earlier synthesized curcumin derivative or silver nanoparticles studies. The aim of the study was to synthesize ST06 coated silver nanoparticles (ST06-AgNPs) using ST06 as both reducing and coating agent.

Methods

The synthesized nanoparticles AgNPs and ST06-AgNPs were characterised for the particle size distribution, morphology, optical properties and surface charge by using UV-visible spectroscopy, dynamic light scattering (DLS) and transmission electron microscopy (TEM). Elemental composition and structural properties were studied by energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction spectroscopy (XRD). The presence of ST06 as capping agent was demonstrated by Fourier transform infrared spectroscopy (FTIR).

Results

The synthesized nanoparticles (ST06-AgNPs) were spherical and had a size distribution in the range of 50–100 nm. UV-Vis spectroscopy displayed a specific silver plasmon peak at 410 nm. The in vitro cytotoxicity effects of ST06 and ST06-AgNPs, as assessed by MTT assay, showed significant growth inhibition of human cervical cancer cell line (HeLa). In addition, studies carried out in EAC tumor-induced mouse model (Ehrlich Ascites carcinoma) using ST06-AgNPs, revealed that treatment of the animals with these nanoparticles resulted in a significant reduction in the tumor growth, compared to the control group animals.

Conclusion

In conclusion, green synthesized ST06-AgNPs exhibited superior anti-tumor efficacy than the free ST06 or AgNPs with no acute toxicity under both in vitro and in vivo conditions. The tumor suppression is associated with the intrinsic apoptotic pathway. Together, the results of this study suggest that ST06-AgNPs could be considered as a potential option for the treatment of solid tumors.

Design, computational studies, synthesis and biological evaluation of thiazole-based molecules as anticancer agents

BACKGROUND

Abolition of cancer warrants effective treatment modalities directed towards specific pathways dysregulated in tumor proliferation and survival. The antiapoptotic Bcl-2 proteins are significantly altered in several tumor types which position them as striking targets for therapeutic intervention. Here we designed, computationally evaluated, synthesized, and biologically tested structurally optimized thiazole-based small molecules as anticancer agents.

METHODS

The virtually designed 200 molecules were subjected to rigorous docking and in silico ADME-Toxicity studies. Out of this, 23 skeletally diverse thiazole-based molecules which passed pan assay interference compounds (PAINS) filter and were synthetically feasible were synthesized in 3 steps using cheap and readily available reagents. The molecules were in vitro evaluated against Bcl-2-Jurkat, A-431 cancerous cell lines and ARPE-19 cell lines. Molecular Dynamics (MD) simulation studies were performed to analyse conformational changes induced by ligand 32 in Bcl-2. Flow cytometry analysis of compound 32 treated Bcl-2 cells was done to check apoptosis.

RESULTS

The molecules exhibited appreciable interactions with Bcl-2 and were having acceptable drug like properties as tested in silico. The multi step synthesis yielded 23 skeletally diverse thiazole-based molecules in up to 80% yield. The molecules simultaneously inhibited Bcl-2 Jurkat cells in vitro without causing detectable toxicity to normal cells (ARPE-19 cells). Among them molecules 32, 50, 53, 57 and 59 showed considerable activities against Bcl-2 Jurkat and A-431cell lines at concentrations ranging from 32-46 μM and 34-52 μM, respectively. The standard doxorubicin exhibited IC₅₀ in Bcl-2 Jurkat and A-431cell lines at 45.87 μM and 42.37 μM, respectively. The molecule 32, almost equipotent in both the cell lines was subjected to molecular dynamics (MD) simulation with Bcl-2 protein (4IEH). It was shown that 32 interacted with protein majorly via hydrophobic interactions and few H-bonding interactions. Fluorescence-activated cell sorting (FACS) analysis established that molecule is dragging cancerous cells towards apoptosis.

DISCUSSION AND CONCLUSION

The chemical intuition was checked by computation coupled with biological results confirmed that thiazole-based hits have the potential to be developed downstream into potent and safer leads against antiapoptotic Bcl-2 cells.

Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus

Nipah virus (NiV) caused several outbreaks in Asian countries including the latest one from Kerala state of India. There is no drug available against NiV till now, despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed the first “anti-Nipah” web resource, which comprising of a data repository, prediction method, and data visualization module. The database contains of 313 (181 unique) chemicals extracted from research articles and patents, which were tested for different strains of NiV isolated from various outbreaks. Moreover, the quantitative structure–activity relationship (QSAR) based regression predictors were developed using chemicals having half maximal inhibitory concentration (IC₅₀). Predictive models were accomplished using support vector machine employing 10-fold cross validation technique. The overall predictor showed the Pearson's correlation coefficient of 0.82 on training/testing dataset. Likewise, it also performed equally well on the independent validation dataset. The robustness of the predictive model was confirmed by applicability domain (William's plot) and scatter plot between actual and predicted efficiencies. Further, the data visualization module from chemical clustering analysis displayed the diversity in the NiV inhibitors. Therefore, this web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly web server is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c _j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c _j formed from >70 different biological activity parameters ( c₀), >300 different drug targets ( c₁), >230 cell lines ( c₂), and 5 organisms of assay ( c₃) or organisms of the target ( c₄). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu⁷⁰, Glu¹³⁹, Leu¹⁴¹, Glu¹⁴⁵, Glu¹⁹⁰, Thr²⁰⁶ and Asp²⁰⁷ were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

Computational screening, ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor

The observable mutated isoforms of epidermal growth factor receptor (EGFR) are important considerable therapeutic benchmarks in moderating the non-small cell lung cancer (NSCLC). Recently, quinazoline-based ATP competitive inhibitors have been developed against the EGFR; however, these imply the mutation probabilities, which contribute to the discovery of high probable novel inhibitors for EGFR mutants. Therefore, SAR-based bioactivity analysis, molecular docking and computational toxicogenomics approaches were performed to identify and evaluate new analogs of gefitinib against the ligand-binding domain of the EGFR double-mutated model. From the diverse groups of molecular clustering and molecular screening strategies, top high-binding gefitinib-analogues were identified and studied against EGFR core cavity through three-phase ensemble docking approach. Resulted high possible leads showed good binding orientations than gefitinib (positive control) thus they were subjected to pharmacophore analysis that possesses possible molecular assets to tight binding with EGFR domain. Residues Ser720, Arg841 and Trp880 were observed as novel hot spots and involved in H-bonds, pi-stacking and π-cation interactions that contribute additional electrostatic potency to sustain stability and complexity of protein-ligand complexes, thus they have ability to profoundly adopted by pharmacophoric features. Furthermore, lead molecules have an inhibition percent probability, anticancer potency, toxic impacts, flexible pharmacokinetics, potential gene-chemical interactions towards EGFR were revealed by computational systems biology tools. Our multiple screening strategies confirmed that the druggable sub-pocket was crucial to strong EGFR-ligand binding. The essential pharmacophoric features of ligands provided viewpoints for new inhibitors envisaging, and predicted scaffolds could used as anticancer agents against selected EGFR mutated isoforms.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.

Open chemoinformatic resources to explore the structure, properties and chemical space of molecules

Open chemoinformatic servers facilitate analysis of chemical space and structure–activity relationships.