A comprehensive meta-analysis of non-coding polymorphisms associated with precancerous lesions and cervical cancer.

OBJECTIVES: To study the risk of polymorphisms present in the non-coding regions of genes related with cervical cancer. METHODS: The PubMed database was extensively searched using text-mining techniques to identify literature containing the association of single nucleotide polymorphisms and cervical cancer. Case-control studies published till June 2020 were considered for the meta-analysis if they fulfilled the selection criteria. The polymorphisms within each case-control study were checked for the presence of genotype data and then divided into groups based on the precancerous and cancerous conditions of the cervix. Odds ratio and 95% confidence intervals (CI) were used to study the effects of polymorphisms with the help of different genetic models (allele, dominant, recessive, heterozygous and homozygous). Also checked heterogeneity along with publication bias and statistical significance using the p-value. RESULTS: 120 papers covering 48 unique non-coding SNPs having 37,123 cases and 39,641 control data was considered for the meta-analysis. The genotype data was categorised into Cancer, Precancer and "Cancer + Precancer" groups, for 43, 8 and 11 SNPs respectively. The meta-analysis identified 21 and 1 SNPs as significant in the Cancer and "Cancer + Precancer" groups. Among all the polymorphisms, rs1143627 (IL1B), rs1800795 (IL6), rs1800871 (IL10), rs568408 (IL12A), rs3312227 (IL12B), rs2275913 (IL17A), rs5742909 (CTLA4), rs1800629 (TNFα), and rs4646903 (CYP1A1) were found to increase risk of cervical cancer in at least three of the five genetic models. CONCLUSION: We identified potential non-coding SNPs corresponding to various cytokines like interleukins (ILs), tumor necrosis factor (TNF), interferon (IFN) and other immune related genes like toll like receptor (TLR), cytotoxic T-lymphocyte associated protein (CTLA) and matrix metalloproteinase (MMP), as significant with increased pooled OR in this meta-analysis pointing to risk association of the immune-related genes in cervical carcinogenesis.

Prioritization and Meta-analysis of regulatory SNPs identified IL6, TGFB1, TLR9 and MMP7 as significantly associated with cervical cancer.

Cervical cancer is a leading women cancer globally with respect to both incidence and mortality. Its increased risk has been linked with HPV infection and genetic variations like single nucleotide polymorphisms (SNPs). Although, studies have been published which evaluates the effect of SNPs in a few candidate genes, however the role of number of regulatory SNPs (rSNPs) in cervical cancer is not available. As literature evidence has shown that non-coding rSNPs are related with increasing cervical cancer risk, we undertook this study to prioritize the important rSNPs and elucidate their role. A search was conducted in PubMed up to December 2020, which led to the identification of 263 articles and 969 SNPs in the non-coding region. These 969 SNPs were analysed through rSNPBase and RegulomeDB, leading to identification of 105 rSNPs. Afterwards, a regulatory module was constructed using protein-protein interaction data and a hub of highly interacting 23 target genes (corresponding to 34 rSNPs) was identified using MCODE. To further understand the mechanism of action of the 34 rSNPs, their transcription factor information with respect to cervical cancer was retrieved. To evaluate the pooled effect of these prioritized polymorphisms in cervical cancer patients, a meta-analysis was performed on 10,537 cases and 11,252 controls from 30 studies corresponding to 8 rSNPs. It led to identification of polymorphisms in IL6 (rs2069837), TGFB1 (rs1800469), TLR9 (rs187084) and MMP7 (rs11568818) which are significantly (p < 0.05) associated with increased cervical cancer risk at the population level. Overall, the study demonstrates that rSNPs targeting immune and inflammatory genes (IL1B, IL6, IL10, IL18, TGFB1, CCR5, CD40, TLR9, and MMP7) are associated with cervical cancer.

Synthesis of metronidazole based thiazolidinone analogs as promising antiamoebic agents.

Metronidazole and its derivatives are widely used for the treatment of amoebiasis. However, metronidazole is considered as the standard drug but it has many side effects. The present study describes the synthesis of a series of metronidazole based thiazolidinone analogs via Knoevenagel condensation of 4-[2-(2-methyl-5-nitro-1H-imidazole-1-yl)ethoxy]benzaldehyde 1 with various thiazolidinone derivatives 2-14 to get the new scaffold (15-27) having better activity and lesser toxicity. Six compounds have shown better efficacy and lesser cytotoxicity than the standard drug metronidazole towards HM1: IMSS strain of Entamoeba histolytica. These compounds may combat the problem of drug resistance and might be effective in identifying potential alternatives for future drug discovery against EhOASS.

Metronidazole hydrazone conjugates: Design, synthesis, antiamoebic and molecular docking studies.

Metronidazole hydrazone conjugates (2-13) were synthesized and screened in vitro for antiamoebic activity against HM1: IMSS strain of Entamoeba histolytica. Six compounds were found to be better inhibitors of E. histolytica than the reference drug metronidazole. These compounds showed greater than 50-60% viability against HeLa cervical cancer cell line after 72 h treatment. Also, molecular docking study was undertaken on E. histolytica thioredoxin reductase (EhTHRase) protein which showed significant binding affinity in the active site. Out of the six actives, some of the compounds showed lipophilic characteristics.

NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database.

Plant-derived molecules have been highly valued by biomedical researchers and pharmaceutical companies for developing drugs, as they are thought to be optimized during evolution. Therefore, we have collected and compiled a central resource Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) that gathers the information related to experimentally validated plant-derived natural compounds exhibiting anti-cancerous activity (in vitro and in vivo), to complement the other databases. It currently contains 1574 compound entries, and each record provides information on their structure, manually curated published data on in vitro and in vivo experiments along with reference for users referral, inhibitory values (IC(50)/ED(50)/EC(50)/GI(50)), properties (physical, elemental and topological), cancer types, cell lines, protein targets, commercial suppliers and drug likeness of compounds. NPACT can easily be browsed or queried using various options, and an online similarity tool has also been made available. Further, to facilitate retrieval of existing data, each record is hyperlinked to similar databases like SuperNatural, Herbal Ingredients' Targets, Comparative Toxicogenomics Database, PubChem and NCI-60 GI(50) data.

Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer.

Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.

Synthesis and evaluation of 7-chloro-4-(piperazin-1-yl)quinoline-sulfonamide as hybrid antiprotozoal agents.

A new series of 4-aminochloroquinoline based sulfonamides were synthesized and evaluated for antiamoebic and antimalarial activities. Out of the eleven compounds evaluated (F1-F11), two of them (F3 and F10) showed good activity against Entamoeba histolytica (IC50 <5 µM). Three of the compounds (F5, F7 and F8) also displayed antimalarial activity against the chloroquine-resistant (FCR-3) strain of Plasmodium falciparum with IC50 values of 2 µM. Compound F7, whose crystal structure was also determined, inhibited ß-haematin formation more potently than quinine. To further understand the action of hybrid molecules F7 and F8, molecular docking was carried out against the homology model of P. falciparum enzyme dihydropteroate synthase (PfDHPS). The complexes showed that the inhibitors place themselves nicely into the active site of the enzyme and exhibit interaction energy which is in accordance with our activity profile data. Application of Lipinski 'rule of five' on all the compounds (F1-F11) suggested high drug likeness of F7 and F8, similar to quinine.

CCDB: a curated database of genes involved in cervix cancer.

The Cervical Cancer gene DataBase (CCDB, http://crdd.osdd.net/raghava/ccdb) is a manually curated catalog of experimentally validated genes that are thought, or are known to be involved in the different stages of cervical carcinogenesis. In spite of the large women population that is presently affected from this malignancy still at present, no database exists that catalogs information on genes associated with cervical cancer. Therefore, we have compiled 537 genes in CCDB that are linked with cervical cancer causation processes such as methylation, gene amplification, mutation, polymorphism and change in expression level, as evident from published literature. Each record contains details related to gene like architecture (exon-intron structure), location, function, sequences (mRNA/CDS/protein), ontology, interacting partners, homology to other eukaryotic genomes, structure and links to other public databases, thus augmenting CCDB with external data. Also, manually curated literature references have been provided to support the inclusion of the gene in the database and establish its association with cervix cancer. In addition, CCDB provides information on microRNA altered in cervical cancer as well as search facility for querying, several browse options and an online tool for sequence similarity search, thereby providing researchers with easy access to the latest information on genes involved in cervix cancer.

Meta-Analysis of 49 SNPs Covering 25,446 Cases and 41,106 Controls Identifies Polymorphisms in Hormone Regulation and DNA Repair Genes Associated with Increased Endometrial Cancer Risk.

Endometrial cancer (EC) is among the most common gynecological disorders globally. As single nucleotide polymorphisms (SNPs) play an important role in the causation of EC, therefore, a comprehensive meta-analysis of 49 SNPs covering 25,446 cases and 41,106 controls was performed to identify SNPs significantly associated with increased EC risk. PubMed was searched to identify case control studies and meta-analysis was performed to compute the pooled odds ratio (OR) at 95% confidence interval (CI). Cochran's Q-test and I2 were used to study heterogeneity, based on which either a random or a fixed effect model was implemented. The meta-analysis identified 11 SNPs (from 10 genes) to be significantly associated with increased EC risk. Among these, seven SNPs were significant in at least three of the five genetic models, as well as three of the polymorphisms (rs1801320, rs11224561, and rs2279744) corresponding to RAD51, PGR, and MDM2 genes, which contained more than 1000 EC cases each and exhibited increased risk. The current meta-analysis indicates that polymorphisms associated with various hormone related genes-SULT1A1 (rs1042028), PGR (rs11224561), and CYP19A1 (rs10046 and rs4775936); DNA repair genes-ERCC2 (rs1799793), OGG1 (rs1052133), MLH1 (rs1800734), and RAD51 (rs1801320) as well as genes like MDM2 (rs2279744), CCND1 (rs9344), and SERPINE1 (rs1799889), are significantly associated with increased EC risk.

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

Identification of Bile Acid-Derived Chemical Chaperone(s) Targeting E46K-Mutated Alpha-Synuclein Protein to Treat Parkinson's Disease: Molecular Modelling, Docking, ADME, and Simulation Studies.

Aggregated α-synuclein (α-syn) present inside small cytoplasmic inclusions in the substantia nigra region marks the major pathological hallmark of Parkinson's disease (PD) and makes it an attractive target for the drug development process. Certain small-molecule chaperones (such as DCA, UDCA, TUDCA) presented the ability to prevent misfolding and aggregation of α-syn as well as to disentangle mature α-syn amyloid fibrils. However, due to toxicity constraints, these small molecules could not be translated into clinical settings. Computational biology methods and bioinformatics approaches allow virtual screening of a large number of molecules, with reduced side effects and better efficacy. In the present study, a library of 10,928 derivatives was generated using DCA, UDCA, and TUDCA bile acid scaffolds and analysed for their binding affinity, pharmacokinetic properties, and drug likeliness profile, to come up with promising compounds with reduced toxicity and better chaperone ability. Molecular docking revealed that with respect to their free binding energy, C1-C25 have the lowest binding energy and bind significantly to recombinantly assembled E46K α-syn fibrils (PDB ID-6UFR). In silico ADME predictions revealed that all these compounds had minimal toxic effects and had good absorption as well as solubility characteristics. Simulation studies further showed that the imidazole ring-based TUDCA derivatives interacted better with the protein in comparison to the others. The proposed study has identified potent chemical chaperones (C2 and C3) as effective therapeutic agents for Parkinson's disease, and further in vitro and in vivo testing will be undertaken to substantiate their potential as novel drugs.

Metronidazole thiosalicylate conjugates: synthesis, crystal structure, docking studies and antiamoebic activity.

Metronidazole thiosalicylate conjugates were synthesized and crystallised in order to discover new molecules having better efficacy than therapeutically administered drug metronidazole, used against Entamoeba histolytica. The three compounds (4-6) showed lower IC(50) values than metronidazole on HM1:IMSS strain of E. histolytica and displayed low cytotoxicity on MCF-7 cell line. In order to get an insight into the mechanisms of action of these compounds, a homology model of E. histolytica thioredoxin reductase (EhTHRase) was constructed and molecular docking was performed into the binding pocket to identify the nature of interactions. The docking studies suggest that the improved inhibitory activity of the newly synthesised metronidazole analogues could be due to involvement of the additional hydrophobic interactions in the binding mode. The result of the present study indicates the molecular fragments that play an essential role in improving the antiamoebic activity.

Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach.

Double mutated epidermal growth factor receptor is a clinically important target for addressing drug resistance in lung cancer treatment. Therefore, discovering new inhibitors against the T790M/L858R (TMLR) resistant mutation is ongoing globally. In the present study, nearly 150 000 molecules from various natural product libraries were screened by employing different ligand and structure-based techniques. Initially, the library was filtered to identify drug-like molecules, which were subjected to a machine learning based classification model to identify molecules with a higher probability of having anti-cancer activity. Simultaneously, rules for constrained docking were derived from three-dimensional protein-ligand complexes and thereafter, constrained docking was undertaken, followed by HYDE binding affinity assessment. As a result, three molecules that resemble interactions similar to the co-crystallized complex were selected and subjected to 100 ns molecular dynamics simulation for stability analysis. The interaction analysis for the 100 ns simulation period showed that the leads exhibit the conserved hydrogen bond interaction with Gln791 and Met793 as in the co-crystal ligand. Also, the study indicated that Y-shaped molecules are preferred in the binding pocket as it enables them to occupy both pockets. The MMGBSA binding energy calculations revealed that the molecules have comparable binding energy to the native ligand. The present study has enabled the identification of a few ADMET adherent leads from natural products that exhibit the potential to inhibit the double mutated drug-resistant EGFR.

Structure-activity Relationship Study on Therapeutically Relevant EGFR Double Mutant Inhibitors.

BACKGROUND: EGFR is a clinically approved drug target in cancer. The first generation tyrosine kinase inhibitors targeting L858R mutated EGFR are routinely used to treat non-small cell lung cancer (NSCLC). However, the presence of a secondary mutation (T790M) tenders these inhibitors ineffective and thus results in the relapse of the disease. OBJECTIVE: New reversible inhibitors are required, which act against T790M/L858R (TMLR) double mutants and overcome resistance. METHOD: In the present study, various Fragment based QSAR (G-QSAR) models along with interaction terms have been studied for amino-pyrimidine derivatives having biological activity against TMLR mutant enzyme. RESULTS: The G-QSAR models developed using partial least squares regression via stepwise forward- backward variable selection technique showed the best results. The model showed a high correlation coefficient (r² = 0.86), cross-validation coefficient (q² = 0.81) and predicted correlation (predicted r² = 0.62), which indicated that the model is robust and predictive. Based on the model, it was revealed that at R1 position increasing saturated carbon (number of -CH atom connected with 3 single bonds i.e. SsssCHcount) and retention index (chi3) is desired for the enhancement of bioactivity. Additionally, at the R2 position, increasing lipophilic character (slogp) and at site R3, the polarizability of compound need to be increased for better inhibitory activity. We further studied the contribution of interactions among significant descriptors in enhancing the activity of the compounds. It revealed that the presence of Sum((R1-SsssCHcount, R2-slogp) and Mult(R1-chi3, R3-polarizabilityAHC) are the most significantly influencing descriptors. We further compared the variation in the most and least active compounds which established that retention of the above properties is essential for imparting significant inhibitory activity to these molecules. CONCLUSION: The study provides site specific information wherein chemical group variation influences the inhibitory potency of TMLR amino-pyrimidine inhibitors, which can be used for designing new molecules with the desired activity.

ADMET profiling of geographically diverse phytochemical using chemoinformatic tools.

Aim: Phytocompounds are important due to their uniqueness, however, only few reach the development phase due to their poor pharmacokinetics. Therefore, preassessing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties is essential in drug discovery. Methodology: Biologically diverse databases (Phytochemica, SerpentinaDB, SANCDB and NuBBEDB) covering the region of India, Brazil and South Africa were considered to predict the ADMET using chemoinformatic tools (Qikprop, pkCSM and DataWarrior). Results: Screening through each of pharmacokinetic criteria resulted in identification of 24 compounds that adhere to all the ADMET properties. Furthermore, assessment revealed that five have potent anticancer biological activity against cancer cell lines. Conclusion: We have established an open-access database (ADMET-BIS) to enable identification of promising molecules that follow ADMET properties and can be considered for drug development.

Inhibitors of Escherichia coli serine acetyltransferase block proliferation of Entamoeba histolytica trophozoites.

The protozoan parasite Entamoeba histolytica is the etiologic agent of amebiasis, a major global public health problem, particularly in developing countries. There is an effective anti-amoebic drug available, however its long term use produces undesirable side effects. As E. histolytica is a micro-aerophilic organism, it is sensitive to high levels of oxygen and the enzymes that are involved in protecting against oxygen-stress are crucial for its survival. Therefore serine acetyltransferase, an enzyme involved in cysteine biosynthesis, was used as a target for identifying potential inhibitors. Virtual screening with Escherichia coli serine acetyltransferase was carried out against the National Cancer Institute chemical database utilizing molecular docking tools such as GOLD and FlexX. The initial analysis yielded 11 molecules of which three compounds were procured and tested for biological activity. The results showed that these compounds partially block activity of the E. coli enzyme and the growth of E. histolytica trophozoites but not mammalian cells.

Synthesis and antiamoebic activity of metronidazole thiosemicarbazone analogues.

Repeated treatment of Entamoeba histolytica infection with commonly used antiamoebic drugs results in not only increasing the toxicity potential but also leads to the development of clinical resistance. Thus new effective agents with less toxicity against amoebiasis are urgently required. With this view, metronidazole thiosemicarbazone analogues 1-11 were synthesized wherein thioamide moiety was substituted by different cyclic and aromatic amines. These compounds were screened against HM1:IMSS strain of E. histolytica parasite cultured in vitro and the sensitivity of the parasite to the metronidazole thiosemicarbazones was evaluated using the microdilution method. Eight compounds (1-4, 7-9 and 11) were found better inhibitors of E. histolytica growth since IC50 values elicited by these compounds were much lower than metronidazole with compound 4 showing the most promising antiamoebic activity (IC50=0.56 microM). The study suggests the beneficial potential of these leads that need to be further explored in order to discover and develop better and yet safer therapeutic agents for amoebiasis.

Control of apoptosis by SMAR1.

The nuclear matrix associated protein SMAR1 is sensitive to p53 and acts as a stress inducer as well as a regulator in the p53 regulatory network. Depending on the amount of stress SMAR1 stimulates, it can drive the p53 dynamics in the system to various dynamical states which correspond to various cellular states. The behavior of p53 in these dynamical states is found to be multifractal, due to the mostly long range correlations and large scale fluctuations imparted by stress. This fractal behavior is exhibited in the topological properties of the networks constructed from these dynamical states, and is a signature of self-organization to optimize information flow in the dynamics. The assortativity found in these networks is due to perturbation induced by stress, and indicates that the hubs in the time series play a significant role in stress management. SMAR1 can also regulate apoptosis in the presence of HDAC1, depending on the stress induced by it.

Synthesis and anti-amoebic activity of gold(I), ruthenium(II), and copper(II) complexes of metronidazole.

A series of Au, Ru, and Cu complexes of metronidazole (= [1-(2-hydroxyethyl)-2-methyl-5-nitro-1H-imidazole; 1) were prepared as highly potent anti-amoebic drugs. The complexes [Au(PPh3)(1)]PF6 (2), [Ru(1)2(Cl)2(H2O)2] (3), and [Cu(1)2(mu-Cl)(H2O)]2Cl2 (4) were readily synthesized from [Au(PPh3)Cl], RuCl3 x 3 H2O, and CuCl2 x 2 H2O, respectively. All complexes were thoroughly characterized by IR, UV/VIS, 1H-NMR, FAB-MS, elemental and thermogravimetric analyses, and, in the case of 4, also by X-ray crystallography (Fig. 1). All complexes were evaluated in vitro as growth inhibitors of Entamoeba histolytica (HM1:IMSS strain). Their IC50 values were in the range of 0.10-0.51 microM (Table 2), which makes these drugs, especially the Cu(II) complex 4, considerably more potent than uncomplexed metronidazole (1; IC50 = 1.81 microM), the current standard drug for the worldwide treatment of amoebiasis.

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.

BACKGROUND: Epidermal Growth Factor Receptor (EGFR) is a well-characterized cancer drug target. In the past, several QSAR models have been developed for predicting inhibition activity of molecules against EGFR. These models are useful to a limited set of molecules for a particular class like quinazoline-derivatives. In this study, an attempt has been made to develop prediction models on a large set of molecules (~3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole. RESULTS: We train, test and validate our classification models on a dataset called EGFR10 that contains 508 inhibitors (having inhibition activity IC50 less than 10 nM) and 2997 non-inhibitors. Our Random forest based model achieved maximum MCC 0.49 with accuracy 83.7% on a validation set using 881 PubChem fingerprints. In this study, frequency-based feature selection technique has been used to identify best fingerprints. It was observed that PubChem fingerprints FP380 (C(~O) (~O)), FP579 (O = C-C-C-C), FP388 (C(:C) (:N) (:N)) and FP 816 (ClC1CC(Br)CCC1) are more frequent in the inhibitors in comparison to non-inhibitors. In addition, we created different datasets namely EGFR100 containing inhibitors having IC50 < 100 nM and EGFR1000 containing inhibitors having IC50 < 1000 nM. We trained, test and validate our models on datasets EGFR100 and EGFR1000 datasets and achieved and maximum MCC 0.58 and 0.71 respectively. In addition, models were developed for predicting quinazoline and pyrimidine based EGFR inhibitors. CONCLUSIONS: In summary, models have been developed on a large set of molecules of various classes for discriminating EGFR inhibitors and non-inhibitors. These highly accurate prediction models can be used to design and discover novel EGFR inhibitors. In order to provide service to the scientific community, a web server/standalone EGFRpred also has been developed ( http://crdd.osdd.net/oscadd/egfrpred/ ).