Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

A pharmacophore describes the framework of molecular features that are vital for the biological activity of a compound. Pharmacophore models are built by using the structural information about the active ligands or targets. The pharmacophore models developed are used to identify novel compounds that satisfy the pharmacophore requirements and thus expected to be biologically active. Drug discovery process is a challenging task that requires the contribution of multidisciplinary approaches. Pharmacophore modeling has been used in various stages of the drug discovery process. The major application areas are virtual screening, docking, drug target fishing, ligand profiling, and ADMET prediction. There are several pharmacophore modeling programs in use. The user must select the right program for the right purpose carefully. There are new developments in pharmacophore modeling with the involvement of the other computational methods. It has been integrated with molecular dynamics simulations. The latest computational approaches like machine learning have also played an important role in the advances achieved. Moreover, with the rapid advance in computing capacity, data storage, software and algorithms, more advances are anticipated. Pharmacophore modeling has contributed to a faster, cheaper, and more effective drug discovery process. With the integration of pharmacophore modeling with the other computational methods and advances in the latest algorithms, programs that have better perfomance are emerging. Thus, improvements in the quality of the pharmacophore models generated have been achieved with these new developments.

Since Schiff base derivatives have a wide range of biological activities, novel Schiff base derivatives were designed and synthesized in satisfactory yields. ¹H NMR, ¹³C NMR, IR, mass and elemental analysis were used to provide a complete structural characterization of the new synthesized Schiff bases (3–6). The antiproliferative activity properties of compounds were tested against two human cancer cell lines including breast (MDA-MB-231) and colon (DLD-1). The compounds overall did not show high cytotoxic activity against both cancer cell lines compared to the positive control drug cisplatin. The synthesized Schiff base compounds were further screened for their in vitro antimicrobial activities against five bacterial strains (Escherichia coli (ATTC 25922), Salmonella thyphimurium (ATTC 14028), Staphylococcus aureus (ATCC 25923), Bacillus subtilis (ATCC 6633), Bacillus cereus (ATCC 11778)) and two fungal strains (Candida albicans (ATCC 10231) and Candida glabrata (ATCC 90030)) using broth micro dilution techniques. The mode of action for the antimicrobial effect in the experimental part was explored through molecular docking. The stability of target-ligand complexes obtained from the docking were assessed through molecular dynamics simulation. The binding affinity of the compounds toward the target protein were also investigated using MMPBSA. Furthermore, electrochemical properties of some compounds was analyzed by DFT calculations. By using POM theory, it becomes more easy to control the bioactivity of drugs. Here, how the physicochemical properties play a crucial role in the orientation of their bioactivity was demonstrated. Communicated by Ramaswamy H. Sarma

In here, a series of pyrimidin-(1(2H)-yl)acetamide and pyrimidin-(1(2H)-yl)isophthalimide derivatives were synthesised according to literature. The cytotoxic activity properties of these molecules were screened against human cancer cell lines as in vitro. The results demonstrated that these molecules had moderate effects on cancer cell lines for 48 h. The binding potential of the relatively active compound, ZF1, towards CDK1 (cyclin-dependent kinase 1) was investigated through molecular docking. The stability of the enzyme-ZF1 complex procured from the molecular docking was assessed through molecular dynamics (MD) simulation. Then, the electrochemical properties of the synthesised compounds were computed through density functional theory (DFT) studies. Computational pharmacokinetic analysis was also performed. The computational studies revealed that ZF1 could bind to CDK1 but its binding potential was less than the standard drug. The MD simulation exhibited that the compound could be stable inside the binding region during the simulation period. ZP3 is predicted to have the highest chemical stability among the synthesised compounds. Furthermore, the computational pharmacokinetic study revealed that ZP1, ZP2, and ZP3 had drug-like properties.

Background: The need to develop novel antimicrobial agents is apparent as infectious diseases are increasing and resistance is rapidly developing against the drugs used in the treatment. Objective: This study aimed at the synthesis, antimicrobial susceptibility testing, and computational elucidation of the mechanism of action of benzoxazole derivatives. It also aimed to compare the results obtained in this study with the previous studies by our group. This would pave the way for designing novel molecules with better antimicrobial activity. The other goal was pharmacophore analysis and in silico ADMET analysis of them. Methods: In this study, synthesis, antimicrobial susceptibility testing, molecular docking, pharmacophore analysis, and ADMET prediction were carried out. Results: The antimicrobial activity studies demonstrated that the synthesized compounds were active against standard strains and clinical isolates at high concentrations. Then, the antimicrobial testing results were compared to similar benzoxazoles tested by our group previously. Benzoxazole derivatives without a methylene bridge between oxazole and phenyl ring were found to be more active than those with the methylene bridge. This was also confirmed by molecular modeling undertaken in this study. The computational results indicated that the antibacterial activity could be achieved by DNA gyrase inhibition. Pharmacophore analysis showed that hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and hydrophobicity features would contribute to the inhibition. In addition, in silico ADMET property investigation of the compounds exhibited that they had the desired pharmacokinetics. Conclusion: Although antibacterial activity by inhibiting DNA gyrase is selective, the synthesized compounds were active at much higher concentrations than the standards. Therefore, in prospective antimicrobial studies, it is better to focus on benzoxazole derivatives without the methylene bridge. Since the compounds had suitable in silico ADMET properties, screening them against the other pharmacologic activities should be carried out. It is recommended to support the molecular modeling results with in vitro or in vivo studies.