In silico modeling and molecular docking insights of kaempferitrin for colon cancer-related molecular targets

Molecular Docking of Seaweed-Derived Drug Fucoxanthin Against the Monkeypox Virus

Background The monkeypox virus (MPXV) is classified as a zoonotic virus of the Poxviridae family, resulting from the MPXV strain of the Orthopoxvirus genus. Seaweeds, or marine macroalgae, are abundant reservoirs of bioactive compounds that demonstrate diverse biological properties, such as antiviral actions. In the field of computational analysis, in silico analysis refers to the use of computer-based methods to study and assess biological systems and processes. To forecast the binding affinity and interaction between the discovered chemical and the target proteins of the MPXV, a molecular docking analysis was conducted. Aim The research aims to conduct an in silico examination of a protein-ligand interaction of a drug produced from seaweed that targets the MPXV.  Methodology Protein Data Bank (PDB) and PubChem databases provided MPXV methyltransferase and fucoxanthin ligand compounds. AutoDockTools 1.5.7 calculated the molecular docking using the Lamarckian genetic algorithm. Autogrid created a grid box around target 8B07 active site hotspot residues. Each docked molecule's docking parameters were obtained from 100 docking experiments with a maximum of 2.5 × 106 energy evaluations, a 0.02 mutation rate, and a 0.8 crossover rate. The population comprised 250 randomly selected volunteers. PyMOL was utilized to observe ligand fragment interactions. Results The binding energy of the ligand fucoxanthin was -5.46 kcal/mol. Fucoxanthin interacts with receptor molecules via hydrogen bonding at the amino acid level: Chain A: PHE188 and TYR189; and Chain B: LYS33, GLN37, GLY38, GLY96, ARG97, PHE115, PRO202, and SER203. The higher the negative docking score, the stronger the binding affinity between the receptor and ligand molecules, indicating that bioactive substances are more effective. Conclusion The findings of this study indicate that fucoxanthin, a pharmaceutical derivative generated from seaweed, had antiviral activity against the MPXV. This conclusion was reached based on protein-ligand interactions.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Synthesis, characterization, anticancer activity and molecular docking of metal complexes bearing a new Schiff base ligand

2-((E)-((4-(((E)-4-Nitrobenzylidene)amino)phenyl)imino)methyl)naphthalen-1-ol, was synthesised followed by metalation with Fe(III), Co(III), Cu(II), Zn(II) and Ni(II) metals. The compounds were characterised by different methods CHN, AAS, IR, NMR, XRD, TGA and UV-Vis. The results reveal that the ligand has bidentate behavior, and it is bound with metals by a coordination bond through both the nitrogen atom of the azomethine group and the oxygen atom, this provided an octahedral geometry. The X-ray diffraction of the compounds indicate that the ligands and complexes of Co(III), Fe(III) and Zn(II) have a crystalline nature, whereas the Ni(II) and Cu(II) have an amorphous structure. The agar diffusion method (hole plate) was used to evaluate the ligand's and its complexes' antibacterial and antifungal effects on Salmonella enterica serovar typhi and Candida albicans, respectively. It was observed that the Fe(III) complex had the best activity among the compounds against microbial strains. Cytotoxicity of new metal complexes was also assessed against A549, HepG-2 and PC-3 cancer cells. Results demonstrated that the Cu(II) complex displayed the preeminent activity among the synthesised compounds against all the tested cell lines. Furthermore, molecular docking simulation revealed that the Fe(III) complex is shown to have a high affinity with the active sites of two targets of microbial strains. Also, the Cu(II) complex shown to has a high affinity with the active sites of three targets of A-549, HepG-2 and PC-3 cancer cells, which was confirmed by the formation of the different modes of interaction.Communicated by Ramaswamy H. Sarma.

Generation of focused drug molecule library using recurrent neural network

CONTEXT

With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the internal chemical structure of molecules, which is similar to a natural language processing task. Although techniques for generating target-specific molecular libraries based on RNN models are mature, research related to drug design and screening continues around the clock. Research based on de novo drug design methods to generate larger quantities of valid compounds is necessary.

METHODS

In this study, a molecular generation model based on RNN was designed, which abandoned the traditional way of stacked RNN and introduced the Nested long short-term memory network structure. To enrich the library of focused molecules for specific targets, we fine-tuned the model using active molecules from novel coronavirus pneumonia and screened the molecules using machine learning models. Following rigorous screening, the selected molecules underwent molecular docking with the SARS-CoV-2 M-pro receptor using AutoDock2.4 to identify the top 3 potential inhibitors. Subsequently, 100-ns molecular dynamics simulations were conducted using Amber22. Molecule parameterization involved the GAFF2 force field, while the proteins were modeled using the ff19SB force field, with solvation facilitated by a truncated octahedral TIP3P solvent environment. Upon completion of molecular dynamics simulations, stability of ligand-protein complexes was assessed by analysis of RMSD, H-bonds, and MM-GBSA. Reasonable results prove that the model can complete the task of de novo drug design and has the potential to be ideal drug molecules.

Abdellattif. Indenyl-thiazole and indenyl-formazan derivatives: Synthesis, anticancer screening studies, molecular-docking, and pharmacokinetic/ molin-spiration properties

Two new series of thiazole and formazan linked to 5-Bromo-indan were synthesized, and their structures were assured based on all possible analytical techniques. The size of the tested derivatives was calculated from the XRD technique and found five derivatives 3, 10a, 14a, 15, and 16 on the nanosized scale. The two series were tested for their efficacy and toxicity as anti-colon and stomach cancers. Derivative 10d showed activity more than the two reference drugs used in the case of SNU-16. Surpislly, in the case of COLO205, five derivatives 4, 6c, 6d, 6e, and 10a are better than the two benchmarks used, and two derivatives, 14a and 14b more potent than cisplatin. All potent derivatives showed a strong fit with the active site of the two tested proteins (gastric cancer (PDB = 2BID) and colon cancer (PDB = 2A4L)) in the molecular docking study. The Pharmacophore and ADME studies of the new derivatives showed that most derivatives revealed promising bioactivity, which indicates the drug-likeness properties against kinase inhibitors, protease, and enzyme inhibitors. In addition, the ProTox-II showed that the four compounds 10d, 16, 6d, and 10a are predicted to have oral LD50 values ranging from 335 to 3500 mg/kg in a rat model with (1 s,4 s)-Eucalyptol bearing the highest values and quercetin holding the lowest one.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.