Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors

Molecular mechanisms of Baihedihuang decoction as a treatment for breast cancer related anxiety: A network pharmacology and molecular docking study

BACKGROUND

The therapeutic effects of a combination of Chinese medicines called Baihedihuang decoction (BD) have been clinically verified, although its molecular targets in breast cancer related anxiety remain unknown.

AIM

To explore the molecular mechanisms of BD for breast cancer related anxiety treatment.

METHODS

We used the Traditional Chinese Medicine Systems Pharmacology database to screen the active ingredients and potential targets of BD, and constructed the "drug-ingredient-target" network map with the help of Cytoscape 3.8 software. Also, we used the Online Mendelian Inheritance in Man, DrugBank, and Gencards databases to collect the disease targets of breast cancer related anxiety, and used the STRING platform to perform protein interaction analysis and construct the protein-protein interaction network. Metascape platform was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of key targets. Molecular docking technology was used to verify the drug component/target disease network.

RESULTS

We screened 16 active ingredients of BD for breast cancer related anxiety, with 113 target proteins. There are 931 disease targets of breast cancer related anxiety, and finally, 43 key targets and 305 Kyoto Encyclopedia of Genes and Genomes pathways were generated. The main active ingredients of BD for breast cancer related anxiety are verbascoside, β-sitosterol, stigmasterol, catalpol, etc. CDK2, TP53, HTR2A, ESR1, etc. are its key targets, and the main involved signaling pathways may include neuroactive ligand-receptor interaction pathway, 5-hydroxytryptaminergic synapse, P53 signaling pathway, cGMP-PKG signaling pathway, the cAMP signaling pathway, etc. Finally, molecular docking was performed with Vina software to validate the key active ingredients in BD with the selected key action targets. The molecular docking results showed that verbascoside, β-sitosterol, stigmasterol and CDK2 could stably bind and interact through amino acid residues SER249, ARG260, PRO228, ALA282, SER276, LYS273, ASN272, etc.

CONCLUSION

The therapeutic effect of BD for breast cancer related anxiety is multi-level, multi-target, and multi-pathway. The findings of this study provide ideas and basis for further research.

Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening

Docking-based virtual screening (VS) is a common starting point in many drug discovery projects. While ligand-based approaches may sometimes provide better results, the advantage of docking lies in its ability to provide reliable ligand binding modes and approximated binding free energies, two factors that are important for hit selection and optimization. Most docking programs were developed to be as general as possible and consequently their performances on specific targets may be sub-optimal. With this in mind, in this work we present a method for the development of target-specific scoring functions using our recently reported Enrichment Optimization Algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations by optimizing an enrichment-like metric. Since EOA requires target-specific active and inactive (or decoy) compounds, we retrieved such data for six targets from the DUD-E database, and used them to re-derive the weights associated with the components that make up GOLD's ChemPLP scoring function yielding target-specific, modified functions. We then used the original ChemPLP function in small-scale VS experiments on the six targets and subsequently rescored the resulting poses with the modified functions. In addition, we used the modified functions for compounds re-docking. We found that in many although not all cases, either rescoring the original ChemPLP poses or repeating the entire docking process with the modified functions, yielded better results in terms of AUC and EF_1% , two metrics, common for the evaluation of VS performances. While work on additional datasets and docking tools is clearly required, we propose that the results obtained thus far hint to the potential benefits in using EOA-based optimization for the derivation of target-specific functions in the context of virtual screening. To this end, we discuss the downsides of the methods and how it could be improved.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods

Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning–based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein–ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein–ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning–based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

The p53 stabilizing agent CP-31398 and multi-kinase inhibitors. Designing, synthesizing and screening of styrylquinazoline series

Quinazoline derivatives constitute a large family of small-molecule inhibitors of tyrosine kinases. In the current study, the p53 protein reactivator CP-31398 was tested against a panel of kinases on the assumption that it was structurally similar to other active inhibitors. Although it was found to be active in the enzyme-based assay, this compound did not block the proliferation of cancer cells at a feasible concentration level. The styrylquinazoline was used to design new structures that might be potential multitarget inhibitors. Subsequently, a series of compounds was obtained and characterized. Their inhibitory activity in a panel of tyrosine kinases had an antiproliferative effect against several cancer cell lines that have different expression levels of those proteins. The mode of protein interaction was tested for the most active compound in docking experiments.