A comprehensive survey of drug-target interaction analysis in allopathy and siddha medicine.

Effective drug delivery is the cornerstone of modern healthcare, ensuring therapeutic compounds reach their intended targets efficiently. This paper explores the potential of personalized and holistic healthcare, driven by the synergy between traditional and allopathic medicine systems, with a specific focus on the vast reservoir of medicinal compounds found in plants rooted in the historical legacy of traditional medicine. Motivated by the desire to unlock the therapeutic potential of medicinal plants and bridge the gap between traditional and allopathic medicine, this survey delves into in-silico computational approaches for studying Drug-Target Interactions (DTI) within the contexts of allopathy and siddha medicine. The contributions of this survey are multifaceted: it offers a comprehensive overview of in-silico methods for DTI analysis in both systems, identifies common challenges in DTI studies, provides insights into future directions to advance DTI analysis, and includes a comparative analysis of DTI in allopathy and siddha medicine. The findings of this survey highlight the pivotal role of in-silico computational approaches in advancing drug research and development in both allopathy and siddha medicine, emphasizing the importance of integrating these methods to drive the future of personalized healthcare.

In silico approaches for drug repurposing in oncology: a scoping review.

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions.

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

A combined in vitro-in silico method for assessing the androgenic activities of bisphenol A and its analogues.

Interactions between endocrine-disruptor chemicals (EDCs) and androgen receptor (AR) have adverse effects on the endocrine system, leading to human reproductive dysfunction. Bisphenol A (BPA) is an EDC that can damage both the environment and human health. Although numerous BPA analogues have been produced as substitutes for BPA, few studies have evaluated their endocrine-disrupting abilities. We assessed the (anti)-androgenic activities of BPA and its analogues using a yeast-based reporter assay. The BPA analogues tested were bisphenol S (BPS), 4-phenylphenol (4PP), 4,4'-(9-fluorenyliden)-diphenol (BPFL), tetramethyl bisphenol F (TMBPF), and tetramethyl bisphenol A (TMBPA). We also conducted molecular docking and dynamics simulations to assess the interactions of BPA and its analogues with the ligand-binding domain of human AR (AR-LBD). Neither BPA nor its analogues had androgenic activity; however, all except BPFL exerted robust anti-androgenic effects. Consistent with the in vitro results, anti-androgenic analogues of BPA formed hydrogen bonding patterns with key residues that differed from the patterns of endogenous hormones, indicating that the analogues display in inappropriate orientations when interacting with the binding pocket of AR-LBD. Our findings indicate that BPA and its analogues disrupt androgen signaling by interacting with the AR-LBD. Overall, BPA and its analogues display endocrine-disrupting activity, which is mediated by AR.

A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment.

The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.

Clinical and Molecular Characterization of Nine Novel Antithrombin Mutations.

Antithrombin (AT) is the major plasma inhibitor of thrombin (FIIa) and activated factor X (FXa), and antithrombin deficiency (ATD) is one of the most severe thrombophilic disorders. In this study, we identified nine novel AT mutations and investigated their genotype-phenotype correlations. Clinical and laboratory data from patients were collected, and the nine mutant AT proteins (p.Arg14Lys, p.Cys32Tyr, p.Arg78Gly, p.Met121Arg, p.Leu245Pro, p.Leu270Argfs*14, p.Asn450Ile, p.Gly456delins_Ala_Thr and p.Pro461Thr) were expressed in HEK293 cells; then, Western blotting, N-Glycosidase F digestion, and ELISA were used to detect wild-type and mutant AT. RT-qPCR was performed to determine the expression of AT mRNA from the transfected cells. Functional studies (AT activity in the presence and in the absence of heparin and heparin-binding studies with the surface plasmon resonance method) were carried out. Mutations were also investigated by in silico methods. Type I ATD caused by altered protein synthesis (p.Cys32Tyr, p.Leu270Argfs*14, p.Asn450Ile) or secretion disorder (p.Met121Arg, p.Leu245Pro, p.Gly456delins_Ala_Thr) was proved in six mutants, while type II heparin-binding-site ATD (p.Arg78Gly) and pleiotropic-effect ATD (p.Pro461Thr) were suggested in two mutants. Finally, the pathogenic role of p.Arg14Lys was equivocal. We provided evidence to understand the pathogenic nature of novel SERPINC1 mutations through in vitro expression studies.

Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches.

Asparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemically stable monoclonal antibodies (mAbs) can substantially mitigate the risk of subsequent failure. In this study, we introduce a novel in silico approach for predicting deamidation and isomerization sites in therapeutic antibodies by analyzing the structural environment surrounding asparagine and aspartate residues. The resulting quantitative structure-activity relationship (QSAR) model was trained using previously published forced degradation data from 57 clinical-stage mAbs. The predictive accuracy of the model was evaluated for four different states of the protein structure: (1) static homology models, (2) enhancing low-frequency vibrational modes during short molecular dynamics (MD) runs, (3) a combination of (2) with a protonation state reassignment, and (4) conventional full-atomistic MD simulations. The most effective QSAR model considered the accessible surface area (ASA) of the residue, the pKa value of the backbone amide, and the root mean square deviations of both the alpha carbon and the side chain. The accuracy was further enhanced by incorporating the QSAR model into a decision tree, which also includes empirical information about the sequential successor and the position in the protein. The resulting model has been implemented as a plugin named "Forecasting Reactivity of Isomerization and Deamidation in Antibodies" in MOE software, completed with a user-friendly graphical interface to facilitate its use.

Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography-mass spectrometry.

BACKGROUND: Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography-mass spectrometry (LC-MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure-property relationships (QSPRs), which were used in this study. RESULTS: A database of 122 antibiotics was selected in which the tR was measured using a Hypersil GOLD column. An optimal three-feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root-mean-square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in-depth explanation of the mechanism of action of each descriptor in predicting the tR is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics. CONCLUSION: The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol-water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC-MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co-operation and Development. © 2024 Society of Chemical Industry.

Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species.

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

Computational methods in glaucoma research: Current status and future outlook.

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Isolation and Identification of a Novel Anti-Dry Eye Peptide from Tilapia Skin Peptides Based on In Silico, In Vitro, and In Vivo Approaches.

Tilapia skin is a great source of collagen. Here, we aimed to isolate and identify the peptides responsible for combating dry eye disease (DED) in tilapia skin peptides (TSP). In vitro cell DED model was used to screen anti-DED peptides from TSP via Sephadex G-25 chromatography, LC/MS/MS, and in silico methods. The anti-DED activity of the screened peptide was further verified in the mice DED model. TSP was divided into five fractions (TSP-I, TSP-II, TSP-III, TSP-IV, and TSP-V), and TSP-II exerted an effective effect for anti-DED. A total of 131 peptides were identified using LC/MS/MS in TSP-II, and NGGPSGPR (NGG) was screened as a potential anti-DED fragment in TSP-II via in silico methods. In vitro, NGG restored cell viability and inhibited the expression level of Cyclooxygenase-2 (COX-2) protein in Human corneal epithelial cells (HCECs) induced by NaCl. In vivo, NGG increased tear production, decreased tear ferning score, prevented corneal epithelial thinning, alleviated conjunctival goblet cell loss, and inhibited the apoptosis of corneal epithelial cells in DED mice. Overall, NGG, as an anti-DED peptide, was successfully identified from TSP, and it may be devoted to functional food ingredients or medicine for DED.

Repurposing Anti-Dengue Compounds against Monkeypox Virus Targeting Core Cysteine Protease.

The monkeypox virus (MPXV) is an enveloped, double-stranded DNA virus belonging to the genus Orthopox viruses. In recent years, the virus has spread to countries where it was previously unknown, turning it into a worldwide emergency for public health. This study employs a structural-based drug design approach to identify potential inhibitors for the core cysteine proteinase of MPXV. During the simulations, the study identified two potential inhibitors, compound CHEMBL32926 and compound CHEMBL4861364, demonstrating strong binding affinities and drug-like properties. Their docking scores with the target protein were -10.7 and -10.9 kcal/mol, respectively. This study used ensemble-based protein-ligand docking to account for the binding site conformation variability. By examining how the identified inhibitors interact with the protein, this research sheds light on the workings of the inhibitors' mechanisms of action. Molecular dynamic simulations of protein-ligand complexes showed fluctuations from the initial docked pose, but they confirmed their binding throughout the simulation. The MMGBSA binding free energy calculations for CHEMBL32926 showed a binding free energy range of (-9.25 to -9.65) kcal/mol, while CHEMBL4861364 exhibited a range of (-41.66 to -31.47) kcal/mol. Later, analogues were searched for these compounds with 70% similarity criteria, and their IC50 was predicted using pre-trained machine learning models. This resulted in identifying two similar compounds for each hit with comparable binding affinity for cysteine proteinase. This study's structure-based drug design approach provides a promising strategy for identifying new drugs for treating MPXV infections.

Identification of Tyrosinase Inhibitory Peptides from Sea Cucumber (Apostichopus japonicus) Collagen by in silico Methods and Study of their Molecular Mechanism.

AIMS: Identify novel tyrosinase inhibitory peptides from sea cucumber (Apostichopus japonicus) collagen using in silico methods and elucidate the molecular interaction mechanism. BACKGROUND: Tyrosinase is a key enzyme in the melanin biosynthesis pathway, to restrain melanin production and reduce the appearance of associated skin diseases, inhibition of tyrosinase activity is one of the most effective methods. OBJECTIVE: The collagen from Apostichopus japonicus, which consists of 3,700 amino acid residues, was obtained from the National Center for Biotechnology Information (NCBI) as the accession number of PIK45888. METHOD: Virtual hydrolyzed method was used, and the peptides generated were compared to the previously established BIOPEP-UWM database. In addition, peptides were examined for their solubility, toxicity, and tyrosinase-binding capacity. RESULT: A tripeptide CME with optimal potential inhibitory activity against tyrosinase was identified, and its inhibitory activity was validated by in vitro experiments. The IC50 value of CME was 0.348 ± 0.02 mM for monophenolase, which was inferior to the positive control peptide glutathione, while it had an IC50 value of 1.436 ± 0.07 mM for diphenolase, which was significantly better than glutathione, and the inhibition effect of CME on tyrosinase was competitive and reversible. CONCLUSION: In silico methods were efficient and useful in the identification of new peptides.

An In Silico Approach toward the Appropriate Absorption Rate Metric in Bioequivalence.

In bioequivalence, the maximum plasma concentration (Cmax) is traditionally used as a metric for the absorption rate, despite the fact that there are several concerns. The idea of "average slope" (AS) was recently introduced as an alternative metric to reflect absorption rate. This study aims to further extend the previous findings and apply an in silico approach to investigate the kinetic sensitivity of AS and Cmax. This computational analysis was applied to the C-t data of hydrochlorothiazide, donepezil, and amlodipine, which exhibit different absorption kinetics. Principal component analysis (PCA) was applied to uncover the relationships between all bioequivalence metrics. Monte Carlo simulations of bioequivalence trials were performed to investigate sensitivity. The appropriate programming codes were written in Python for the PCA and in MATLAB® for the simulations. The PCA verified the desired properties of AS and the unsuitability of Cmax to reflect absorption rate. The Monte Carlo simulations showed that AS is quite sensitive to detecting differences in absorption rate, while Cmax has almost negligible sensitivity. Cmax fails to reflect absorption rate, and its use in bioequivalence gives only a false impression. AS has the appropriate units, is easily calculated, exhibits high sensitivity, and has the desired properties of absorption rate.

Evaluation of piperine analogs against prostate cancer targeting AKT1 kinase domain through network pharmacological analysis.

Prostate cancer is the second most fatal malignancy in men after lung cancer, and the fifth leading cause of death. Piperine has been utilized for its therapeutic effects since the time of Ayurveda. According to traditional Chinese medicine, piperine has a wide variety of pharmacological effects, including anti-inflammatory, anti-cancer, and immune-regulating properties. Based on the previous study, Akt1 (protein kinase B) is one of the targets of piperine, it belongs to the group of oncogenes and the mechanism of the Akt1 is an interesting approach for anticancer drug design. From the peer-reviewed literature, five piperine analogs were identified altogether, and a combinatorial collection was formed. However, may not be entirely clear how piperine analogs work to prevent prostate cancer. In the present study, serine-threonine kinase domain Akt1 receptor was employed to analyze the efficacy of piperine analogs against standards using in silico methodologies. Additionally, their drug-likeness was evaluated utilizing online servers like Molinspiration and preADMET. Using AutoDock Vina, the interactions of five piperine analogs and two standards with Akt1 receptor was investigated. Our study reveals that piperine analog-2 (pip2) shows highest binding affinity (- 6.0 kcal/mol) by forming 6 hydrogen bonds with more hydrophobic interactions compared to other four analogs and standards. In conclusion, the piperine analog pip2, which shows strong inhibition affect in Akt1-cancer pathway, may be employed as chemotherapeutic drugs.

MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem.

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter 'u'. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation.

In Vitro and Predictive Computational Toxicology Methods for the Neurotoxic Pesticide Amitraz and Its Metabolites.

The Varroa destructor parasite is responsible for varroasis in honeybees worldwide, the most destructive disease among parasitic diseases. Thus, different insecticides/acaricides have been widely used within beehives to control these parasitic diseases. Namely, amitraz is the most used acaricide due to its high efficacy shown against Varroa destructor. However, pesticides used for beehive treatments could be incorporated into the honey and accumulate in other hive products. Hence, honeybee health and the impairment of the quality of honey caused by pesticides have gained more attention. Amitraz and its main metabolites, N-(2,4-dimethylphenyl) formamide (2,4-DMF) and 2,4-dimethylaniline (2,4-DMA), are known to be potent neurotoxicants. In this research, the cytotoxicity of amitraz and its metabolites has been assessed by MTT and PC assays in HepG2 cells. In addition, possible target receptors by in silico strategies have been surveyed. Results showed that amitraz was more cytotoxic than its metabolites. According to the in silico ADMEt assays, amitraz and its metabolites were predicted to be compounds that are able to pass the blood-brain barrier (BBB) and induce toxicity in the central and peripheral nervous systems. The main target class predicted for amitraz was the family of A G protein-coupled receptors that comprises responses to hormones and neurotransmitters. This affects, among other things, reproduction, development, locomotion, and feeding. Furthermore, amitraz and its metabolites were predicted as active compounds interacting with diverse receptors of the Tox21-nuclear receptor signaling and stress response pathways.

Bioinformatics of the Endocannabinoid System: Study of DNA Methylation at Rat Cnr1 Gene Promoter.

In this chapter, we will describe the bioinformatic tools that allow verifying the presence of CpG islands in a gene promoter region. We will also describe the tools needed to identify consensus motifs for specific transcription factors, focusing on the study of rat type-1 cannabinoid receptor gene (R_Cnr1) as a case study.

In silico screening of Annona Muricata L. Leaf constituents and derivatives towards selected targets of Type 2 Diabetes Mellitus

BACKGROUND@#Type 2 diabetes mellitus, or T2DM, is one of the world's most chronic health problems that is linked to numerous deaths and high health care expenses. 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), protein-tyrosine phosphatase 1B (PTP1B) and mono-ADP-ribosyl transferase sirtuin-6 (SIRT6) were among the novel proteins and focus targets of diabetes research. Annona muricata is a commonly used natural remedy for several illnesses, including type 2 diabetes mellitus. However, most of these traditional claims have received few molecular evaluations.@*OBJECTIVES@#This investigated the phytoconstituents and derivatives of the leaves of A. muricata by evaluating their binding profiles towards selected novel T2DM-related protein targets through in silico methods.@*METHODOLOGY@#This study screened the potential lead compounds from the leaves of A. muricata by evaluating the binding energies of the parent compounds and derivatives with the targets compared to the native ligands and known substrates through molecular docking simulations. Additionally, pharmacokinetic, physicochemical properties, and binding interactions were also assessed using several software programs and online databases.@*RESULTS@#Out of the 8 selected parent compounds of Annona muricata, a total of 672 derivatives were designed, tested, and compared against the controls for at least one of the three protein targets. Among these, 280 derivatives exhibited more negative binding energies than controls in each protein target.@*CONCLUSION@#The designed derivatives can be synthesized and further investigated for potential biological effects towards 11β-HSD1, PTP1B, and SIRT6 through in vitro and in vivo experiments.

Docking is not enough: 17-trifluoromethylphenyl trinor PGF2α is only a very weak ligand of neurokinin-1 receptor.

17-trifluoromethylphenyl trinor prostaglandin F2α (17-CF3PTPGF2α) was reported recently to exhibit in vitro and in vivo anticancer activity. Based solely on the results of in silico molecular docking, it was claimed that this compound is NK1 receptor (NK1R) antagonist and that its activity is through this receptor. In this contribution we show that 17-CF3PTPGF2α is only a very weak NK1R ligand (IC50 > 200 µM). In connection with that we discuss the issue of this compound's molecular target. Finally, we briefly narrate on the proper use of molecular docking in biomedical research.