Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

Multipotent AChE and BACE-1 inhibitors for the treatment of Alzheimer's disease: Design, synthesis and bio-analysis of 7-amino-1,4-dihydro-2H-isoquilin-3-one derivates.

In this paper, the preparation of a new class of multi-target-directed ligands (MTDLs) based on a 7-amino-1,4-dihydro-2H-isoquilin-3-one, whose lead (compound I) showed promising properties in acetylcholinesterase (AChE) inhibitory activity [1], is described. The results of in vitro activities and molecular docking demonstrated that the target molecule (compounds 10a-n) with three parts of aromatic moieties and appropriate structural length can interact with aromatic residues in catalytic active site (CAS), peripheral anionic site (PAS) and the channel of AChE. And the introduce of connecting amide bonds, enables the target molecules provide sufficient hydrogen bond donors and acceptors to interact with the catalytic site of BACE-1. Notably, compound 10d exerted excellent AChE inhibition (IC50 = 18.93 ± 1.02 pM, 181-fold more inhibitory effect compared with donepezil), BACE-1 inhibition (97.68 ± 8.01% at 20 µM), and good metal chelating property, which can be chosen as lead compound for further optimization of novel small ligand for the treatment of Alzheimer's disease.