TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico

Rapid advancing computational technologies have greatly speeded up the development of computer-aided drug design (CADD). Recently, pharmaceutical companies have increasingly shifted their attentions toward traditional Chinese medicine (TCM) for novel lead compounds. Despite the growing number of studies on TCM, there is no free 3D small molecular structure database of TCM available for virtual screening or molecular simulation. To address this shortcoming, we have constructed TCM Database@Taiwan (http://tcm.cmu.edu.tw/) based on information collected from Chinese medical texts and scientific publications. TCM Database@Taiwan is currently the world's largest non-commercial TCM database. This web-based database contains more than 20,000 pure compounds isolated from 453 TCM ingredients. Both cdx (2D) and Tripos mol2 (3D) formats of each pure compound in the database are available for download and virtual screening. The TCM database includes both simple and advanced web-based query options that can specify search clauses, such as molecular properties, substructures, TCM ingredients, and TCM classification, based on intended drug actions. The TCM database can be easily accessed by all researchers conducting CADD. Over the last eight years, numerous volunteers have devoted their time to analyze TCM ingredients from Chinese medical texts as well as to construct structure files for each isolated compound. We believe that TCM Database@Taiwan will be a milestone on the path towards modernizing traditional Chinese medicine.National Science Council of Taiwan (NSC 99-2221-E-039-013-)China Medical UniversityAsia UniversityAsia University (CMU98-ASIA-09)Taiwan. Dept. of Health (Clinical Trial and Research Center of Excellence (DOH99-TD-B-111-004))Taiwan. Dept. of Health (Cancer Research Center of Excellence (DOH99-TD-C-111-005)

Hybrid neural network approaches to predict drug–target binding affinity for drug repurposing: screening for potential leads for Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug–target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein–protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA

Weighted Equation and Rules - A Novel Concept for Evaluating Protein-Ligand Interaction

[[abstract]]In this study, a novel methodology for evaluating protein-ligand interaction and quantitated the traditional Chinese medicine (TCM) by Yin-Yang theory are proposed and investigated by a case report of the human epidermal growth factor receptor 2 (HER2)-ligand. Inhibitors (n = 176) of HER2 from references with a broad range of activities (IC50) were employed to the docking program to calculate the binding affinities. The docking score of twelve scoring functions versus actual pIC(50) plot were regressed. According to the weighted rules, the coefficient of determinations (R-2) from the regression analysis of each scoring function and pIC(50) were chosen as the weights in the weighted equation. The R-2 (0.5858) of weighted score (WS) versus actual pIC(50), was statistically higher than that of the consensus score (CS) (R-2 = 0.2441). The WS method lies in combining the scoring functions from different algorithms to evaluate the sum of binding affinities that is more comprehensive than any single scoring function can achieve. The WS calculated by equation successfully shows a statically significant correlation with good predictability. Thus, this methodology might provide a persuasive virtual screening criterion to evaluate the protein-ligand interaction and quantitative analysis of the functions for Chinese medicine in the future

Virtual Screening and Drug Design for PDE-5 Receptor from Traditional Chinese Medicine Database

[[abstract]]Erectile dysfunction (ED) is a sexual disorder mainly caused by decrease in cellular concentration of cyclic guanosine monophosphate (cGMP), which is degraded by phosphodiesterase type-5 (PDE-5). As a potent therapeutic target, inhibitors such as Viagra (R), Cialis (R), and Levitra (R) have already been developed to target PDE-5 for treating ED; traditional Chinese medicine, Epimedium sagittatum. also has shown prominent results as well. To developed new PDE-5 inhibitors, we performed a virtual screening of traditional Chinese medicine (TCM) database and docking analyses to identify candidates. Known PDE-5 inhibitors were used to construct a three dimensional quantitative structure-activity relationship (3D QSAR) model by HypoGen program. From docking analyses, isochlorogenic acid b was identified as the most potential inhibitory compound. De novo evolution designed 47 derivatives. Of the 47 derivatives, seven were able to map into the pharmacophore model. and these seven compounds were suggested to be the most promising leads for inhibiting PDE-5. An analysis of the hydrogen bond interactions formed between the docked ligands and PDE-5 identified ASN662, SER663 and GLN817 as the most frequently interacting residues. A total of eight novel leading compounds were identified to have favorable interaction with PDE-5. These compounds all had hydrogen bond interactions with three key residues that could be further investigated for understanding of PDE-5 and ligands interaction

In Silico

The peroxisome proliferator-activated receptors (PPARs) related to regulation of lipid metabolism, inflammation, cell proliferation, differentiation, and glucose homeostasis by controlling the related ligand-dependent transcription of networks of genes. They are used to be served as therapeutic targets against metabolic disorder, such as obesity, dyslipidemia, and diabetes; especially, PPAR-γ is the most extensively investigated isoform for the treatment of dyslipidemic type 2 diabetes. In this study, we filter compounds of traditional Chinese medicine (TCM) using bioactivities predicted by three distinct prediction models before the virtual screening. For the top candidates, the molecular dynamics (MD) simulations were also utilized to investigate the stability of interactions between ligand and PPAR-γ protein. The top two TCM candidates, 5-hydroxy-L-tryptophan and abrine, have an indole ring and carboxyl group to form the H-bonds with the key residues of PPAR-γ protein, such as residues Ser289 and Lys367. The secondary amine group of abrine also stabilized an H-bond with residue Ser289. From the figures of root mean square fluctuations (RMSFs), the key residues were stabilized in protein complexes with 5-Hydroxy-L-tryptophan and abrine as control. Hence, we propose 5-hydroxy-L-tryptophan and abrine as potential lead compounds for further study in drug development process with the PPAR-γ protein

Lightweight equivariant interaction graph neural network for accurate and efficient interatomic potential and force predictions

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. A century ago, Felix Bloch demonstrated how leveraging the equivariance of the translation operation on a crystal lattice (with geometric symmetry) could significantly reduce the computational cost of determining wavefunctions and accurately calculate material properties. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions in crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By extracting both local and global structures from vector representations and learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Finally, to further validate the predicted interatomic potentials from our model, we conduct classical molecular dynamics (MD) and ab initio MD simulation across various systems, including solid, liquid, and gas. It is found that LEIGNN can achieve the accuracy of ab initio MD and retain the computational efficiency of classical MD across all examined systems, demonstrating its accuracy, efficiency, and universality