Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure–activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.

We extracted 15 pterosin derivatives from Pteridium aquilinum that inhibited β-site amyloid precursor protein cleaving enzyme 1 (BACE1) and cholinesterases involved in the pathogenesis of Alzheimer’s disease (AD). (2R)-Pterosin B inhibited BACE1, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with an IC₅₀ of 29.6, 16.2 and 48.1 µM, respectively. The K_i values and binding energies (kcal/mol) between pterosins and BACE1, AChE, and BChE corresponded to the respective IC₅₀ values. (2R)-Pterosin B was a noncompetitive inhibitor against human BACE1 and BChE as well as a mixed-type inhibitor against AChE, binding to the active sites of the corresponding enzymes. Molecular docking simulation of mixed-type and noncompetitive inhibitors for BACE1, AChE, and BChE indicated novel binding site-directed inhibition of the enzymes by pterosins and the structure−activity relationship. (2R)-Pterosin B exhibited a strong BBB permeability with an effective permeability (P_e) of 60.3×10⁻⁶ cm/s on PAMPA-BBB. (2R)-Pterosin B and (2R,3 R)-pteroside C significantly decreased the secretion of Aβ peptides from neuroblastoma cells that overexpressed human β-amyloid precursor protein at 500 μM. Conclusively, our study suggested that several pterosins are potential scaffolds for multitarget-directed ligands (MTDLs) for AD therapeutics.

Sauchinone, an active lignan isolated from the aerial parts of Saururus chinensis (Saururaceae), exhibits anti-inflammatory, anti-obesity, anti-hyperglycemic, and anti-hepatic steatosis effects. As herb–drug interaction (HDI) through cytochrome P450s (CYPs)-mediated metabolism limits clinical application of herbs and drugs in combination, this study sought to explore the enzyme kinetics of sauchinone towards CYP inhibition in in vitro human liver microsomes (HLMs) and in vivo mice studies and computational molecular docking analysis. In in vitro HLMs, sauchinone reversibly inhibited CYP2B6, 2C19, 2E1, and 3A4 activities in non-competitive modes, showing inhibition constant (K_i) values of 14.3, 16.8, 41.7, and 6.84 μM, respectively. Also, sauchinone time-dependently inhibited CYP2B6, 2E1 and 3A4 activities in vitro HLMs. Molecular docking study showed that sauchinone could be bound to a few key amino acid residues in the active site of CYP2B6, 2C19, 2E1, and 3A4. When sibutramine, clopidogrel, or chlorzoxazone was co-administered with sauchinone to mice, the systemic exposure of each drug was increased compared to that without sauchinone, because sauchinone reduced the metabolic clearance of each drug. In conclusion, when sauchinone was co-treated with drugs metabolized via CYP2B6, 2C19, 2E1, or 3A4, sauchinone–drug interactions occurred because sauchinone inhibited the CYP-mediated metabolic activities.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases and a widespread form of dementia. Aggregated forms of the amyloid β-peptide (Aβ) are identified as a toxic species responsible for neuronal damage in AD. Extensive research has been conducted to reveal the aggregation mechanism of Aβ. However, the structure of pathological aggregates and the mechanism of aggregation are not well understood. Recently, experimental studies have confirmed that the α-sheet structure in Aβ drives aggregation and toxicity in AD. However, how the α-sheet structure is formed in Aβ and how it contributes to Aβ aggregation remains elusive. In the present study, molecular dynamics simulations suggest that Aβ adopts the α-strand conformation by peptide-plane flipping. Multiple α-strands interact through hydrogen bonding to form α-sheets. This structure acts as a nucleus that initiates and promotes aggregation and fibrillation of Aβ. Our findings are supported by previous experimental as well as theoretical studies. This study provides valuable structural insights for the design of anti-AD drugs exploiting the α-strand/α-sheet structure.