Current Computer - Aided Drug Design - Volume 19, Issue 5, 2023

Background: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predicting drug-target interactions have been introduced successfully to solve these problems and have received extensive attention. Objective: In this paper, we provided a summary of databases that are useful in DTI prediction and intend to concentrate on machine learning methods as a chemogenomic approach in drug discovery. Unlike previous surveys, we propose a comparative analytical framework based on the evaluation criteria. Methods: In our suggested framework, there are three stages to follow: First, we present a comprehensive categorization of machine learning-based techniques as a chemogenomic approach for drug-target interaction prediction problems; Second, to evaluate the proposed classification, several general criteria are provided; Third, unlike other surveys, according to the evaluation criteria introduced in the previous stage, a comparative analytical evaluation is performed for each approach. Results: This systematic research covers the earliest, most recent, and outstanding techniques in the DTI prediction problem and identifies the advantages and weaknesses of each approach separately. Additionally, it can be helpful in the effective selection and improvement of DTI prediction techniques, which is the main superiority of the proposed framework. Conclusion: This paper gives a thorough overview to serve as a guide and reference for other researchers by providing an analytical framework which can help to select, compare, and improve DTI prediction methods.

Background: Rheumatoid arthritis (RA) is an inflammatory autoimmune disease that affects the synovial joints. Nearly 1.6 billion patients are affected by RA worldwide and the incidence of RA is about 0.5 to 1%. Recent studies reveal that immune cell responses and secretion of inflammatory factors are important for the control of RA. Methods: In this study, a set of 402 phytochemicals with anti-inflammatory properties and 16 target proteins related to anti-inflammatory diseases were identified from the literature and they were subjected to network analysis. The protein-protein interaction (PPI) network was constructed using STRING (Search Tool for the Retrieval of Interacting Genes database) database. Visualization of the target gene-phytochemical network and its protein-protein interaction network was conducted using Cytoscape and further analyzed using MCODE (Molecular Complex Detection). The gene ontology and KEGG pathway analysis was performed using DAVID tool. Results: Our results from the network approach indicate that the phytochemicals such as Withanolide, Diosgenin, and Butulin could act as potential substitute for anti-inflammatory drugs, including DMARDs. Genes such as Mitogen-activated protein kinase (MAPK) and Interleukin were found as hub genes and acted as best inhibitors for the target protein pathways. Curcumin, Catechin was also found to be involved in various signaling pathways such as NF-kappa B signaling pathway, ErbB signaling pathway and acted as the best inhibitor along with other candidate phytochemicals. Conclusion: In the current study, we were able to identify Withanolide, Diosgenin, and Butulin as potential anti-inflammatory phytochemicals and determine their association with key pathways involved in RA through network analysis. We hypothesized that natural compounds could significantly contribute to the reduction of dosage, improve the treatment and act as a therapeutic agent for more economical and safer treatment of RA.

Aim: To provide new methods and ideas for the clinical application of integrated traditional Chinese and Western medicine in the treatment of esophageal cancer. Background: Traditional Chinese medicine compound Kushen injection (CKI) has been widely used in the clinic with adjuvant radiotherapy and chemotherapy. However, the mechanism of action of CKI as adjuvant therapy for esophageal cancer has not yet been described. Methods: This study is based on network pharmacology, data mining, and molecular docking technology to explore the mechanism of action of CKI in the treatment of esophageal cancer. We obtained the effective ingredients and targets of CKI from the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP) and esophageal cancer-related genes from the Online Mendelian Inheritance in Man (OMIM) and GeneCards databases. Results: CKI mainly contains 58 active components. Among them, the top 5 active ingredients are quercetin, luteolin, naringenin, formononetin, and beta-sitostero. The target protein of the active ingredient was matched with the genes associated with esophageal cancer. The active ingredients targeted 187 esophageal cancer target proteins, including AKT1, MAPK1, MAPK3, TP53, HSP90AA1, and other proteins. Then, we enriched and analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and used AutoDockVina to dock the core targets and compounds. Finally, PyMOL and Ligplot were used for data visualization. Conclusion: This study provides a new method and ideas for the clinical application of integrated traditional Chinese and Western medicine in the treatment of esophageal cancer.

Background: Amphotericin B is a gold-standard drug, particularly for the treatment of systemic fungal infections. However, its low solubility and permeability limit its application. To improve its bioavailability, AmB may be conjugated with various water-soluble auxiliary groups. Methods: Custom R group Enumeration was used at the designated sites of Amphotericin B. The designated sites taken into consideration are the carboxyl moiety of the aglycone part and the amine moiety of the glycone part of Amphotericin B for Enumeration purposes. The enumerated molecules were subjected to QikProp properties. Results: We identified fourteen hits with improved predicted aqueous solubility and cell permeability. Conclusion: Enumeration might be applicable in improving bioavailability, which could lead to the oral formulation of the Amphotericin B drug.

Background: The rapidly widespread SARS-CoV-2 infection has affected millions worldwide, thus becoming a global health emergency. Although vaccines are already available, there are still new COVID-19 cases daily worldwide, mainly due to low immunization coverage and the advent of new strains. Therefore, there is an utmost need for the discovery of lead compounds to treat COVID-19. Objective: Considering the relevance of the SARS-CoV-2 M^PRO in viral replication and the role of the isoquinoline moiety as a core part of several biologically relevant compounds, this study aimed to identify isoquinoline-based molecules as new drug-like compounds, aiming to develop an effective coronavirus inhibitor. Methods: 274 isoquinoline derivatives were submitted to molecular docking interactions with SARS-CoV-2 M^PRO (PDB ID: 7L0D) and drug-likeness analysis. The five best-docked isoquinoline derivatives that did not violate any of Lipinskie's or Veber's parameters were submitted to ADMET analysis and molecular dynamics (MD) simulations. Results: The selected compounds exhibited docking scores similar to or better than chloroquine and other isoquinolines previously reported. The fact that the compounds interact with residues that are pivotal for the enzyme's catalytic activity, and show the potential to be orally administered makes them promising drugs for treating COVID-19. Conclusion: Ultimately, MD simulation was performed to verify ligand-protein complex stability during the simulation period.