Inherent efficacies of pyrazole-based derivatives for cancer therapy: the interface between experiment and .

There has been an increasing trend in the design of novel pyrazole derivatives for desired biological applications. For a cost-effective strategy, scientists have implemented various computational drug design tools to go hand in hand with experiments for the design and discovery of potentially effective pyrazole-based therapeutics. This review highlights the milestones of pyrazole-containing inhibitors and the use of molecular modeling techniques in conjunction with experimental studies to provide a view of the binding mechanism of these compounds.

Exploring the effect of ritonavir and TMC-310911 on SARS-CoV-2 and SARS-CoV main proteases: potential from a molecular perspective.

Aim: As coronavirus (CoV) disease 2019-associated pneumonia spreads globally, there has been an urgent need to combat the spread and develop vaccines.

Materials & Methods: We used an integrated computational algorithm to explore the binding mechanism of TMC-310911/ritonavir (RVT) with SARS-CoV-2 and SARS-CoV main proteases.

Results: RVT and TMC-310911 had favorable interactions with the proteases, and these high interactions are facilitated by some significant residues such as Asn133, Gly195 and Gln192.

'Piperazining' the catalytic gatekeepers: unraveling the pan-inhibition of SRC kinases; LYN, FYN and BLK by masitinib.

Blocking oncogenic signaling of B-cell receptor (BCR) has been explored as a viable strategy in the treatment of diffuse large B-cell lymphoma. Masitinib is shown to multitarget LYN, FYN and BLK kinases that propagate BCR signals to downstream effectors. However, the molecular mechanisms of its selectivity and pan-inhibition remain elusive.

Co-inhibition as a strategic therapeutic approach to overcome rifampin resistance in tuberculosis therapy: atomistic insights.

Aim: Amid the current global challenge of antimicrobial resistance, RNA polymerase remains a paramount therapeutic target for tuberculosis. Dual binding of rifampin (RIF) and a novel compound, DAAP1, demonstrated the suppression of RIF resistance. However, a paucity of data elucidating the structural mechanism of action of this synergistic interaction prevails.

Reversible versus irreversible inhibition modes of ERK2: a comparative analysis for ERK2 protein kinase in cancer therapy.

Aim: Irreversible covalent drug inhibition is an emerging paradigm; however, critical gaps in unraveling the efficacy of molecular determinants still persist.

Methodology: We compare two ERK2 inhibitors with different binding modes. A 5-7-Oxozeaenol is selective inhibitor which irreversibly binds ERK2 by the formation of covalent bond with Cys166 while 5-iodotubercidin binds noncovalently.

Hybrid 2D/3D-quantitative structure-activity relationship and modeling studies perspectives of pepstatin A analogs as cathepsin D inhibitors.

Aim: Cathepsin D, one of the attractive targets in the treatment of breast cancer, has been implicated in HIV neuropathogenesis with potential proteolytic effects on chemokines. Methodology/result: Diverse modeling tools were used to reveal the key structural features affecting the inhibitory activities of 78 pepstatin A analogs. Analyses were performed to investigate the stability, rationality and fluctuation of the analogs.

Novel quinazolinone-based 2,4-thiazolidinedione-3-acetic acid derivatives as potent aldose reductase inhibitors.

Aim: Targeting aldose reductase enzyme with 2,4-thiazolidinedione-3-acetic acid derivatives having a bulky hydrophobic 3-arylquinazolinone residue.

Materials & Methods: All the target compounds were structurally characterized by different spectroscopic methods and microanalysis, their aldose reductase inhibitory activities were evaluated, and binding modes were studied by molecular modeling.

Results: All the synthesized compounds proved to inhibit the target enzyme potently, exhibiting IC values in the nanomolar/low nanomolar range.

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry.

Tailored-pharmacophore model to enhance virtual screening and drug discovery: a case study on the identification of potential inhibitors against drug-resistant Mycobacterium tuberculosis (3R)-hydroxyacyl-ACP dehydratases.

Aim: Virtual screening (VS) is powerful tool in discovering molecular inhibitors that are most likely to bind to drug targets of interest. Herein, we introduce a novel VS approach, so-called 'tailored-pharmacophore', in order to explore inhibitors that overcome drug resistance. Methodology & results: The emergence and spread of drug resistance strains of tuberculosis is one of the most critical issues in healthcare.

Per-residue energy decomposition pharmacophore model to enhance virtual screening in drug discovery: a study for identification of reverse transcriptase inhibitors as potential anti-HIV agents.

A novel virtual screening approach is implemented herein, which is a further improvement of our previously published "target-bound pharmacophore modeling approach". The generated pharmacophore library is based only on highly contributing amino acid residues, instead of arbitrary pharmacophores, which are most commonly used in the conventional approaches in literature. Highly contributing amino acid residues were distinguished based on free binding energy contributions obtained from calculation from molecular dynamic (MD) simulations.