Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors.

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors.

Background: Overexpression of LSD1 is associated with the occurrence of many diseases, including cancers, which makes LSD1 a significant target for anticancer drug research. Methodology & Results: ... Graphical abstract

The stimulator of interferon genes (STING) plays a significant role in immune defense and protection against tumor proliferation. Many cyclic dinucleotide (CDN) analogues have been reported to regulate its activity, but the dynamic process involved when the ligands activate STING remains unclear. In this work, all-atom molecular dynamics simulations were performed to explore the binding mode between human STING (hSTING) and four cyclic adenosine–inosine monophosphate analogs (cAIMPs), as well as 2′,3′-cGMP-AMP (2′,3′-cGAMP). The results indicate that these cAIMPs adopt a U-shaped configuration within the binding pocket, forming extensive non-covalent interaction networks with hSTING. These interactions play a significant role in augmenting the binding, particularly in interactions with Tyr167, Arg238, Thr263, and Thr267. Additionally, the presence of hydrophobic interactions between the ligand and the receptor further contributes to the overall stability of the binding. In this work, the conformational changes in hSTING upon binding these cAIMPs were also studied and a significant tendency for hSTING to shift from open to closed state was observed after binding some of the cAIMP ligands.

BDD demonstrates high sensitivity, high selectivity and rapid response performance in the detection of NO. It has been successfully utilized for imaging both exogenous and endogenous NO in cells and zebrafish.

Hepatitis C virus (HCV) infection is a global health concern, with HCV exhibiting high variability across eight major genotypes and multiple subtypes. A series of benzofuran inhibitors have been proven to exhibit effective antiviral effects on HCV NS5B polymerase of multiple genotypes. Given the pan-genotypic inhibitory potential exhibited by such inhibitors, this study concentrates on the development of novel and effective pan-genotypic inhibitors against HCV NS5B polymerase. Through conducting virtual screening based on molecular structure similarity and docking, potential lead compounds ZINC103262402, ZINC215816615, and ZINC215876403 were identified. Molecular dynamics simulations and binding free energy calculations indicated that these compounds can stably bind to HCV NS5B polymerases of various genotypes. ADMET prediction revealed that ZINC215816615 and ZINC215876403 possess superior pharmacokinetic profiles. Further investigation into the binding mode of the benzofuran analogs with NS5B polymerase facilitated the design of new molecules, BFD-01 and BFD-02, through fragment growth. The groups of BFD-01 and BFD-02 extended on the basis of retaining the structure of the lead compound, enhancing hydrophobicity, and forming new hydrogen bonds to improve the binding ability. This research provides valuable insights for the development of novel pan-genotypic HCV NS5B polymerase inhibitors.