In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation

In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization

The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation. The models are developed with the hybrid optimal descriptors i.e. using both SMILES and hydrogen-suppressed molecular graph (HSG). The QSAR model developed for split 3 is selected as a prominent model ([Formula: see text]= 0.727, [Formula: see text]= 0.628, [Formula: see text]= 0.642, and [Formula: see text]=0.615). The model is interpreted mechanistically by identifying the characteristics responsible for the promoter of the increase or decrease. The structural attributes as promoters of increase of pIC₅₀ were aliphatic carbon atom connected to double-bound (C…=…, aliphatic oxygen atom connected to aliphatic carbon (O…C…), branching on aromatic ring (c…(…), and aliphatic nitrogen (N…). The pIC₅₀ of eight natural flavonols with pIC₅₀ more than 4.0, were predicted by the best model. The molecular docking is also performed for natural flavonols on the PC-3 cell line using the protein (PDB: 3RUK).

Exploring N-myristoyltransferase as a promising drug target against parasitic neglected tropical diseases

Neglected tropical diseases (NTDs) constitute a group of approximately 20 infectious diseases that mainly affect the impoverished population without basic sanitation in tropical countries. These diseases are responsible for many deaths worldwide, costing billions of dollars in public health investment to treat and control these infections. Among them are the diseases caused by protozoa of the Trypanosomatid family, which constitute Trypanosoma cruzi (Chagas disease), Trypanosoma brucei (sleeping sickness), and Leishmaniasis. In addition, there is a classification of other diseases, called the big three, AIDS, tuberculosis, and malaria, which are endemic in countries with tropical conditions. Despite the high mortality rates, there is still a gap in the treatment. The drugs have a high incidence of side effects and protozoan resistance, justifying the investment in developing new alternatives. In fact, the Target-Based Drug Design (TBDD) approach is responsible for identifying several promising compounds, and among the targets explored through this approach, N-myristoyltransferase (NMT) stands out. It is an enzyme related to the co-translational myristoylation of N-terminal glycine in various peptides. The myristoylation process is a co-translation that occurs after removing the initiator methionine. This process regulates the assembly of protein complexes and stability, which justifies its potential as a drug target. In order to propose NMT as a potential target for parasitic diseases, this review will address the entire structure and function of this enzyme and the primary studies demonstrating its promising potential against Leishmaniasis, T. cruzi, T. brucei, and malaria. We hope our information can help researchers worldwide search for potential drugs against these diseases that have been threatening the health of the world's population.

Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria. Green alga Pseudokirchneriella subcapitata has been used for ecotoxicological studies to assess the toxicity of different toxic chemicals in freshwater. Quantitative Structure Activity Relationships (QSAR) are mathematical models to relate chemical structure and activity/physicochemical properties of chemicals quantitatively. Herein, Quantitative Structure Toxicity Relationship (QSTR) modeling is applied to assess the toxicity of a data set of 334 different chemicals on Pseudokirchneriella subcapitata, in terms of EC₁₀ and EC₅₀ values. The QSTR models are established using CORAL software by utilizing the target function (TF₂) with the index of ideality of correlation (IIC). A hybrid optimal descriptor computed from SMILES and molecular hydrogen-suppressed graphs (HSG) is employed to construct QSTR models. The results of various statistical parameters of the QSTR model developed for pEC₁₀ and pEC₅₀ range from excellent to good and are in line with the standard parameters. The models prepared with IIC for Split 3 are chosen as the best model for both endpoints (pEC₁₀ and pEC₅₀). The numerical value of the determination coefficient of the validation set of split 3 for the endpoint pEC₁₀ is 0.7849 and for the endpoint pEC₅₀, it is 0.8150. The structural fractions accountable for the toxicity of chemicals are also extracted. The hydrophilic attributes like 1…n…(… and S…(… Created by potrace 1.16, written by Peter Selinger 2001-2019 ]]> … exert positive contributions to controlling the aquatic toxicity and reducing algal toxicity, whereas attributes such as c…c…c…, C…C…C… enhance lipophilicity of the molecules and consequently enhance algal toxicity.

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria.

Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling

Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.

The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors

Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC₅₀) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r²) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC₅₀ values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC₅₀ values were predicted based on the average prediction of ten models.