Current Topics in Medicinal Chemistry - Volume 21, Issue 28, 2021

Imidazole has an important five-membered aromatic heterocyclic ring, which is available widely in natural products and synthetic molecules. The special structural characteristics of imidazole ring enable it to bind with a variety of enzymes and receptors through hydrogen bonds, coordination, ion-dipole and cation-π interactions, hydrophobic effects, and Van der Waals forces. These interactions promote several biological activities involving anti-tumor, anti-inflammatory, anti-microbial, and anti-viral properties. Herein, we review and discuss recent developments in using imidazole derivatives and their special pharmacological activities for the treatment of various diseases.

Thiazolidinedione, an important heterocyclic ring system, is a pharmacophore and a privileged scaffold in medicinal chemistry. Researchers have shown its potential application as a backbone for inhibitors, as it is involved in anticancer development strategies, cancer progression and metastasis. In this paper, the anticancer activities of thiazolidinedione derivatives were reviewed for the first time with respect to different substituents and their positions. This work may imitate a stirring wheel to guide further advanced development of new anticancer molecules with high efficacy.

1,3,4-thiadiazole is a five-membered aromatic heterocycle containing two nitrogen atoms and one sulfur atom. As a privileged scaffold, it has its unique chemical properties and biological characteristics. In the design of drugs, they are widely and flexibly applied by medicinal chemists, and many candidates with therapeutic prospects have been developed. In this review, we focus on 1,3,4-thiadiazole derivatives and their various biological activities reported in the past five years (from 2015 to early 2020), such as anticancer, antibacterial, antifungal, anti-tuberculosis, antiinflammatory, antivirus, anti-leishmania and other functions. It is believed that this review can provide some new ideas for seeking rational design to develop 1,3,4-thiadiazole based medicinal agents with better activity and lower toxicity.

Peptidomimetics are studied for medicinal application because of their ability to mimic hierarchical structures of peptides and proteins. To break the limitation and expand the peptidomimetics family, a new class of peptidomimetics based on peptide nucleic acids (PNAs) backbone - “γ-AApeptides” was developed. Compared with previous peptidomimetics, γ-AApeptides possess prominent advantages such as resistance to proteolytic degradation, enhanced chemodiversity, good selectivity and outstanding bioactivity. The synthesis of γ-AApeptides is carried out using a ‘‘monomer building block’’ strategy which is facile and efficient. γ-AApeptides are able to mimic primary and secondary structures of therapeutic peptides, which make them promising candidates for molecular probes and potential drug leads. In the past decade, several interesting structures and applications of γ-AApeptides have been developed by different approaches such as structure-based design, combinatorial library screening, and peptides selfassembly and folding. By following the mechanism of host-defense peptides (HDPs), antibiotic γ- AApeptides showed broad-spectrum activity. At the same time, γ-AApeptides can be used for combinatorial library screening because of their structural stability and their chemodiversity. Anticancer agents, anti-T2DM (Type 2 diabetes mellitus) agents, anti-HIV (human immuno-deficiency virus) agents and anti-Alzheimer’s disease agents were developed by combinatorial screening and rational design. Furthermore, γ-AApeptides as biopolymers, nanomaterials, supramolecular structures and self-assembly architectures were studied due to their unique backbone structures. Therefore, γ-AApeptides may play an important role in the development of peptidomimetics.

With the rapid development of computer science in scopes of theory, software, and hardware, artificial intelligence (mainly in form of machine learning and more complex deep learning) combined with advanced cheminformatics is playing an increasingly important role in drug discovery process. This development has also facilitated privileged scaffold-related research. By definition, a privileged scaffold is a structure that frequently occurs in diverse bioactive molecules, either has a diverse family affinity or is selective to multiple family members in a superfamily, whilst it is different from the“frequent hitters”, or the “pan-assay interference compounds”. The long history of the use of this concept has witnessed a functional shift from stand-alone technology towards an integrated component in the drug discovery toolbox. Meanwhile, continuous efforts have been dedicated to deepening the understandings of the features of known privileged scaffolds. In this contribution, we focus on the current privileged scaffold-related research driven by state-of-art artificial intelligence approaches and cheminformatics. Representative cases with an emphasis on distinct research aspects are presented, including an update of the knowledge on privileged scaffolds, proofof- concept tools, and workflows to identify privileged scaffolds and to carry on de novo design, informatic SAR models with diversely complex data sets to provide an instructive prediction on new potential molecules bearing privileged scaffolds.