Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors

Global QSAR modelling was performed to predict the pIC₅₀ values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF₁ (W_IIC = 0) and TF₂ (W_IIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF₂ are more reliable and robust and were used to predict the pIC₅₀ values. The model constructed for split 4 via TF₂ is regarded as the best model and the numerical values of r²_Train, r²_Valid, Q²_Train and Q²_Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC₅₀ of BTK inhibitors were also identified.

Molecular Docking Compounds of Cinnamaldehyde Derivatives as Anticancer Agents

Objective:

Cinnamaldehyde (CM) has a molecular structure with the main reaction center of an aromatic ring which the bioactivity can be modified as an anticancer agent by substituting the groups in the ortho (o), meta (m), and para (p) position. The present study aimed to investigate the correlation of the cluster region that was substituted in CM on its activity for various anticancer receptors.

Methods:

The receptor types used in the test were 5FL6, 1HOV, 4GY7, 5EAM, 4XCU, 4EL9, and 4PQW. The suitability of the hydroxy (OH) and methoxy (OMe) groups, which were substituted, was studied based on the value of Ki, their interactions with metal cofactors, and the type of amino acid residues that function as cancer receptor inhibitors. The docking was conducted using AutoDock 4.

Results:

The study results showed that all derivative compounds (o, m, and p) –OH and –OMe CM commonly had better anticancer activities than CM. o-OH CM has the best activity against receptors 5FL6, 1HOV, 4GY7, 5EAM, and 4XCU, and m-OMe CM has better activity against the 4EL9 receptors when compared with other CM derivatives.

Conclusion:

Based on this study, the compound derived from CM, i.e. OHC, tends to show the best anticancer activity.

Recent advances in pharmacological diversification of Src family kinase inhibitors

Background

Src kinase, a nonreceptor protein-tyrosine kinase is composed of 11 members (in human) and is involved in a wide variety of essential functions required to sustain cellular homeostasis and survival.

Main body of the abstract

Deregulated activity of Src family kinase is related to malignant transformation. In 2001, Food and Drug Administration approved imatinib for the treatment of chronic myeloid leukemia followed by approval of various other inhibitors from this category as effective therapeutics for cancer patients. In the past decade, Src family kinase has been investigated for the treatment of diverse pathologies in addition to cancer. In this regard, we provide a systematic evaluation of Src kinase regarding its mechanistic role in cancer and other diseases. Here we comment on preclinical and clinical success of Src kinase inhibitors in cancer followed by diabetes, hypertension, tuberculosis, and inflammation.

Short conclusion

Studies focusing on the diversified role of Src kinase as potential therapeutical target for the development of medicinally active agents might produce significant advances in the management of not only various types of cancer but also other diseases which are in demand for potent and safe therapeutics.

Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that they also mediate noncovalent interactions. Aiming at a deep understanding of PAINS promiscuity, we performed an analysis of the Protein Data Bank to characterize the binding modes of PAINS. We explored the binding mode conservation of 34 PAINS classes present in 871 ligands and among 517 protein targets. The two major findings of this work are the following: First, different PAINS classes exhibit different levels of binding mode conservation. Our novel classification of PAINS based on binding mode similarity enables a rational assessment of PAINS from a structural perspective. Second, PAINS classes with variable binding modes can bind with high affinity. The evaluation of noncovalent binding modes of PAINS-like compounds sheds light on the mechanisms of promiscuous binding. Our findings could facilitate the decisions on how to deal with PAINS and help scientists to understand why PAINS produce hits in their screenings.

In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

In silico design and analysis of NS4B inhibitors against hepatitis C virus

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.Communicated by Ramaswamy H. Sarma.

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.