Molecular docking is a structure-based computational method that generates the binding pose and affinity between ligands and targets. There are many powerful docking programs. However, there is no single program that is suitable for every system. Hence, an appropriate program is chosen based on availability, need, and computer capacity. Molecular docking has clear steps that should be followed carefully to get a good result. : Molecular docking has many applications at various stages in drug discovery. Although it has various application areas, it is commonly applied in virtual screening and drug repurposing. As a result, it is playing a substantial role in the endeavor to discover a potent drug against COVID-19. There are also approved drugs in the pharmaceutical market that are developed through the use of molecular docking. As the accessible data is increasing and the method is advancing with the contribution of the latest computational developments, its use in drug discovery is also increasing. : Molecular docking has played a crucial role in making drug discovery faster, cheaper, and more effective. More advances in docking algorithms, integration with other computational methods, and the introduction of new approaches are expected. Thus, more applications that will make drug discovery easier are expected.

Background: Nonadherence to medication in tuberculosis (TB) hampers optimal treatment outcomes. Digital health technology (DHT) seems to be a promising approach to managing problems of nonadherence to medication and improving treatment outcomes. Objective: This paper systematically reviews the effect of DHT in improving medication adherence and treatment outcomes in patients with TB. Methods: A literature search in PubMed and Cochrane databases was conducted. Randomized controlled trials (RCTs) that analyzed the effect of DHT interventions on medication adherence outcomes (treatment completion, treatment adherence, missed doses, and noncompleted rate) and treatment outcomes (cure rate and smear conversion) were included. Adult patients with either active or latent TB infection were included. The Jadad score was used for evaluating the study quality. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline was followed to report study findings. Results: In all, 16 RCTs were selected from 552 studies found, and 6 types of DHT interventions for TB were identified: 3 RCTs examined video directly observed therapy (VDOT), 1 examined video-observed therapy (VOT), 1 examined an ingestible sensor, 1 examined phone call reminders, 2 examined medication monitor boxes, and 8 examined SMS text message reminders. The outcomes used were treatment adherence, including treatment completion, treatment adherence, missed dose, and noncompleted rate, as well as clinical outcomes, including cure rate and smear conversion. In treatment completion, 4 RCTs (VDOT, VOT, ingestible sensor, SMS reminder) found significant effects, with odds ratios and relative risks (RRs) ranging from 1.10 to 7.69. Treatment adherence was increased in 1 study by SMS reminders (RR 1.05; 95% CI 1.04-1.06), and missed dose was reduced in 1 study by a medication monitor box (mean ratio 0.58; 95% CI 0.42-0.79). In contrast, 3 RCTs of VDOT and 3 RCTs of SMS reminders did not find significant effects for treatment completion. Moreover, no improvement was found in treatment adherence in 1 RCT of VDOT, missed dose in 1 RCT of SMS reminder, and noncompleted rate in 1 RCT of a monitor box, and 2 RCTs of SMS reminders. For clinical outcomes such as cure rate, 2 RCTs reported that phone calls (RR 1.30; 95% CI 1.07-1.59) and SMS reminders (OR 2.47; 95% CI 1.13-5.43) significantly affected cure rates. However, 3 RCTs found that SMS reminders did not have a significant impact on cure rate or smear conversion. Conclusions: It was found that DHT interventions can be a promising approach. However, the interventions exhibited variable effects regarding effect direction and the extent of improving TB medication adherence and clinical outcomes. Developing DHT interventions with personalized feedback is required to have a consistent and beneficial effect on medication adherence and outcomes among patients with TB.

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.

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.

Nine 2-(1,2-benzothiazol-3-yl)-N-(4-oxo-2-phenyl-1,3-thiazolidin-3-yl)propanamides combining a benzisothiazole and 4-thiazolidinone in one framework were designed and synthesized. The aim of the study was to verify their effectiveness to affect the inflammatory/oxidative process in which free oxygen and nitrite (ROS and RNS) radicals, inflammatory mediators, such as nuclear factor κB (NF-κB), and matrix metalloproteinases (MMPs) are involved. Docking studies of all the compounds were performed in order to explore their binding mode at the MMP-9 protein. An appreciable anti-inflammatory/potential wound healing effects of the tested compounds was highlighted. Derivative 23, bearing a 4-carboxyphenyl substituent at C2 of the 4-thiazolidinone ring, exhibited the highest activity, being able to inhibit MMP-9 at nanomolar level(IC₅₀ = 40 nM).

Protease-activated receptor 1 (PAR1) has been established as a promising target in many diseases, including various cancers. Strong evidence also suggests its role in metastasis. It is proved experimentally that PAR1 can induce numerous cell phenotypes, i.e. proliferation and differentiation. A strong link between PAR1 gene overexpression and high levels of ß-catenin was suggested by a study of the PAR1–Gα(13)–DVL axis in ß-catenin stabilization in cancers. An in vitro study was carried out to analyze PAR1 expression by flow cytometry on CD38+138+ plasma cells obtained from patients either at diagnosis (n: 46) (newly diagnosed multiple myeloma (NDMM)) or at relapse (n: 45) (relapsed/refractory multiple myeloma (RRMM)) and compared with the controls. Our previously synthesized benzoxazole (XT2B) and benzamide (XT5) derivatives were tested with in vitro 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays, which revealed significant inhibitory activity on PAR1. We provide docking studies using Autodock Vina of these newly tested compounds to compare with the known PAR1 inhibitors in order to examine the binding mechanisms. In addition, the docking results are validated using HYDE binding assessment and a neural network (NN) scoring function.