PubChem as a public resource for drug discovery

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

Solamargine improves the therapeutic efficacy of anti-PD-L1 in lung adenocarcinoma by inhibiting STAT1 activation

OBJECTIVE

The effect of solamargine on lung adenocarcinoma and its effect on STAT1 signaling pathway mediated immune escape were studied through network pharmacology and in vitro and in vivo experiments.

METHODS

The solamargine targets were screened using the TCMSP and the LUAD targets were screened using the GeneCard, OMIM, PharmGkb, TTD and DrugBank databases. PPI network analysis and target prediction were performed using GO and KEGG. Colony formation assay, EDU staining, wound healing, transwell assay, Hoechst and flow cytometry were used to detect the effects of solamargine on the proliferation, migration and apoptosis of LUAD. Western blotting (WB) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) were used to detect P-STAT1 and PD-L1 expression. And immunofluorescence was used to detect P-STAT1 expression. In vivo experiments, C57BL/6 mice were divided into control group, low concentration group, high concentration group, positive control group and combination group. Every other day, following seven consecutive doses, the size of the tumor was assessed. Finally, the expressions of P-STAT1, STAT1, PD-L1 and apoptosis index proteins were detected by WB.

RESULTS

The anti-LUAD effect of solamargine was found by wound healing, colony formation assay, transwell assay, hoechst and EdU staining. The results of network pharmacological analysis showed that solamargine could suppress STAT1 expression level. Further enrichment assay of STAT1 showed that STAT1 was associated with immune-related pathways. In addition, molecular signal analysis by WB and RT-qPCR indicated that solamargine could reduce the expression levels of P-STAT1 and PD-L1 in a concentration-dependent manner. According to the results of in vivo assays, combination of solamargine and immune checkpoint inhibitors (ICIs) durvalumab could significantly inhibit the growth of Lewis transplanted tumors in C57BL/6 mice, and no toxic side effect was recoded.

CONCLUSION

These results indicated that solamargine could inhibit the proliferation and promote the apoptosis of LUAD. It also could reduce the expression level of P-STAT1 protein and inhibit the expression level of PD-L1. At the same time, the combination with the ICIs can better block the expression of PD-L1 in cells, thereby inhibiting the immune escape pathway of tumor cells and achieving anti-tumor effects. This study proposed a novel combined therapeutic approach, involving the inhibition of STAT1 by solamargine in conjunction with ICIs.

DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.

Astaxanthin targets IL-6 and alleviates the LPS-induced adverse inflammatory response of macrophages

Numerous natural compounds are recognized for their anti-inflammatory properties attributed to antioxidant effects and the modulation of key inflammatory factors. Among them, astaxanthin (AST), a potent carotenoid antioxidant, remains relatively underexplored regarding its anti-inflammatory mechanisms and specific molecular targets. In this study, human monocytic leukemia cell-derived macrophages (THP-1) were selected as experimental cells, and lipopolysaccharides (LPS) served as inflammatory stimuli. Upon LPS treatment, the oxidative stress was significantly increased, accompanied by remarkable cellular damage. Moreover, LPSs escalated the expression of inflammation-related molecules. Our results demonstrate that AST intervention could effectively alleviate LPS-induced oxidative stress, facilitate cellular repair, and significantly attenuate inflammation. Further exploration of the anti-inflammatory mechanism revealed AST could substantially inhibit NF-κB translocation and activation, and mitigate inflammatory factor production by hindering NF-κB through the antioxidant mechanism. We further confirmed that AST exhibited protective effects against cell damage and reduced the injury from inflammatory cytokines by activating p53 and inhibiting STAT3. In addition, utilizing network pharmacology and in silico calculations based on molecular docking, molecular dynamics simulation, we identified interleukin-6 (IL-6) as a prominent core target of AST anti-inflammation, which was further validated by the RNA interference experiment. This IL-6 binding capacity actually enabled AST to curb the positive feedback loop of inflammatory factors, averting the onset of possible inflammatory storms. Therefore, this study offers a new possibility for the application and development of astaxanthin as a popular dietary supplement of anti-inflammatory or immunomodulatory function.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Quercetin is a Potential Therapy for Rheumatoid Arthritis via Targeting Caspase-8 Through Ferroptosis and Pyroptosis

Background

Rheumatoid arthritis (RA) is one of the most common chronic inflammatory autoimmune diseases. However, the underlying molecular mechanisms of its pathogenesis are unknown. This study aimed to identify the common biomarkers of ferroptosis and pyroptosis in RA and screen potential drugs.

Methods

The RA-related differentially expressed genes (DEGs) in GSE55235 were screened by R software and intersected with ferroptosis and pyroptosis gene libraries to obtain differentially expressed ferroptosis-related genes (DEFRGs) and differentially expressed pyroptosis-related genes (DEPRGs). We performed Gene Ontology (GO), Kyoto Encyclopedia of the Genome (KEGG), ClueGO, and Protein-Protein Interaction (PPI) analysis for DEFRGs and DEPRGs and validated them by machine learning. The microRNA/transcription factor (TF)-hub genes regulatory network was further constructed. The key gene was validated using the GSE77298 validation set, cellular validation was performed in in vitro experiments, and immune infiltration analysis was performed using CIBERSORT. Network pharmacology was used to find key gene-targeting drugs, followed by molecular docking and molecular dynamics simulations to analyze the binding stability between small-molecule drugs and large-molecule proteins.

Results

Three hub genes (CASP8, PTGS2, and JUN) were screened via bioinformatics, and the key gene (CASP8) was validated and obtained through the validation set, and the diagnostic efficacy was verified to be excellent through the receiver operating characteristic (ROC) curves. The ferroptosis and pyroptosis phenotypes were constructed by fibroblast-like synoviocytes (FLS), and caspase-8 was detected and validated as a common biomarker for ferroptosis and pyroptosis in RA, and quercetin can reduce caspase-8 levels. Quercetin was found to be a potential target drug for caspase-8 by network pharmacology, and the stability of their binding was further verified using molecular docking and molecular dynamics simulations.

Conclusion

Caspase-8 is an important biomarker for ferroptosis and pyroptosis in RA, and quercetin is a potential therapy for RA via targeting caspase-8 through ferroptosis and pyroptosis.

Computational identification of potential inhibitors targeting cdk1 in colorectal cancer

Introduction: Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Methods: Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Results: Four hit compounds, namely, spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. Discussion: The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.

Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel

Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.

Analysis of random mutations in Salmonella Gallinarum dihydropteroate synthase conferring sulfonamide resistance

In bacteria and primitive eukaryotes, sulfonamide antibiotics block the folate pathway by inhibiting dihydropteroate synthase (FolP) that combines para-aminobenzoic acid (pABA) and dihydropterin pyrophosphate (DHPP) to form dihydropteroic acid (DHP), a precursor for tetrahydrofolate synthesis. However, the emergence of resistant strains has severely compromised the use of pABA mimetics as sulfonamide drugs. Salmonella enterica serovar Gallinarum (S. Gallinarum) is a significant source of antibiotic-resistant infections in poultry. Here, a sulfonamide-resistant FolP mutant library of S. Gallinarum was generated through random mutagenesis. Among resistant strains, substitution of amino acid Arginine 171 with Proline (R171P) in the FolP protein conferred the highest resistance against sulfonamide. Substitution of Phe28 with Leu or Ile (F28L/I) led to modest sulfonamide resistance. Structural modeling indicates that R171P and Phenylalanine 28 with leucine or isoleucine (F28L/I) substitution mutations are located far from the substrate-binding site and cause insignificant conformational changes in the FolP protein. Rather, in silico studies suggest that the mutations altered the stability of the protein, potentially resulting in sulfonamide resistance. Identification of specific mutations in FolP that confer resistance to sulfonamide would contribute to our understanding of the molecular mechanisms of antibiotic resistance.

Identification of Natural Lead Compounds against Hemagglutinin-Esterase Surface Glycoprotein in Human Coronaviruses Investigated via MD Simulation, Principal Component Analysis, Cross-Correlation, H-Bond Plot and MMGBSA

The pandemic outbreak of human coronavirus is a global health concern that affects people of all ages and genders, but there is currently still no effective, approved and potential drug against human coronavirus, as many other coronavirus vaccines have serious side effects while the development of small antiviral inhibitors has gained tremendous attention. For this research, HE was used as a therapeutic target, as the spike protein displays a high binding affinity for both host ACE2 and viral HE glycoprotein. Molecular docking, pharmacophore modelling and virtual screening of 38,000 natural compounds were employed to find out the best natural inhibitor against human coronaviruses with more efficiency and fewer side effects and further evaluated via MD simulation, PCA, DCCR and MMGBSA. The lead compound ‘Calceolarioside B’ was identified on the basis of pharmacophoric features which depict favorable binding (ΔGbind −37.6799 kcal/mol) with the HE(5N11) receptor that describes positive correlation movements in active site residues with better stability, a robust H-bond network, compactness and reliable ADMET properties. The Fraxinus sieboldiana Blume plant containing the Calceolarioside B compound could be used as a potential inhibitor that shows a higher efficacy and potency with fewer side effects. This research work will aid investigators in the testing and identification of chemicals that are effective and useful against human coronavirus.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Potential Therapeutic Agents on Alzheimer's Disease through Molecular Docking and Molecular Dynamics Simulation Study of Plant-Based Compounds

Globally Alzheimer's disease (AD) is a highly complex, heterogeneous, and multifactorial neurological disease. AD is categorized clinically through a steady loss in memory and progressive decline of cognitive function. So far, there is no effective cure is available for the treatment of AD. Here, we identified Plant-based compounds (PBCs) from seven therapeutic plants through pharmacophore and pharmacokinetics approaches. Subsequently, we retrieved 65 AD associated proteins by Text Mining approach .We observed the interactions between 39 PBCs with 65 AD-associated targets by using molecular docking. Further, we carried out Molecular dynamics simulation analysis to predict the steady binding of top drug-target complexes. The entire MD simulation results analysis was evidence that seven drug-target complexes consistently interacted during the in silico experiment. The top complexes were the target CHLE interacted with 2 PBCs (Pseudojujubogenin and Anahygrine), target VDAC1 interacted with Withanolide R, target THOP1 interacted with Withaolide R, target AOFB interacted with 2 PBCs (Nardostachysin and Viscosalactone B), and target ACHE interacted with the drug (12-Deoxywithastramonolide). These PBCs have stably and flexibly interacted at the protein's active site region. Our results suggest that these PBCs and targets are potential therapeutic candidates for molecular development in AD.

Network pharmacology and molecular docking-based analysis of protective mechanism of MLIF in ischemic stroke

Objective

This study aimed to evaluate the potential mechanism by which Monocyte locomotion inhibitory factor (MLIF) improves the outcome of ischemic stroke (IS) inflammatory injury.

Methods

Potential MLIF-related targets were predicted using Swiss TargetPrediction and PharmMapper, while IS-related targets were found from GeneCards, PharmGKB, and Therapeutic Target Database (TTD). After obtaining the intersection from these two datasets, the Search Tool for Retrieval of Interacting Genes/Protein (STRING11.0) database was used to analyze the protein-protein interaction (PPI) network of the intersection and candidate genes for MLIF treatment of IS. The candidate genes were imported into the Metascape database for Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The top 20 core genes and the “MLIF-target-pathway” network were mapped using the Cytoscape3.9.1. Using AutoDock Vina1.1.2, the molecular docking validation of the hub targets and MLIF was carried out. In the experimental part, transient middle cerebral artery occlusion (tMCAO) and oxygen and glucose deprivation (OGD) models were used to evaluate the protective efficacy of MLIF and the expression of inflammatory cytokines and the putative targets.

Results

MLIF was expected to have an effect on 370 targets. When these targets were intersected with 1,289 targets for ischemic stroke, 119 candidate therapeutic targets were found. The key enriched pathways were PI3K-Akt signaling pathway and MAPK signaling pathway, etc. The GO analysis yielded 1,677 GO entries (P < 0.01), such as hormone stimulation, inflammatory response, etc. The top 20 core genes included AKT1, EGFR, IGF1, MAPK1, MAPK10, MAPK14, etc. The result of molecular docking demonstrated that MLIF had the strong binding capability to JNK (MAPK10). The in vitro and in vivo studies also confirmed that MLIF protected against IS by lowering JNK (MAPK10) and AP-1 levels and decreasing pro-inflammatory cytokines (IL-1, IL-6).

Conclusion

MLIF may exert a cerebral protective effect by inhibiting the inflammatory response through suppressing the JNK/AP-1 signaling pathway.

Application of DFT Calculations in Designing Polymer-Based Drug Delivery Systems: An Overview

Drug delivery systems transfer medications to target locations throughout the body. These systems are often made up of biodegradable and bioabsorbable polymers acting as delivery components. The introduction of density functional theory (DFT) has tremendously aided the application of computational material science in the design and development of drug delivery materials. The use of DFT and other computational approaches avoids time-consuming empirical processes. Therefore, this review explored how the DFT computation may be utilized to explain some of the features of polymer-based drug delivery systems. First, we went through the key aspects of DFT and provided some context. Then we looked at the essential characteristics of a polymer-based drug delivery system that DFT simulations could predict. We observed that the Gaussian software had been extensively employed by researchers, particularly with the B3LYP functional and 6-31G(d, p) basic sets for polymer-based drug delivery systems. However, to give researchers a choice of basis set for modelling complicated organic systems, such as polymer-drug complexes, we then offered possible resources and presented the future trend.

Wheat grain proteomic and protein-metabolite interactions analyses provide insights into plant growth promoting bacteria-arbuscular mycorrhizal fungi-wheat interactions

Proteomic, protein-protein and protein-metabolite interaction analyses in wheat inoculated with PGPB and AMF identified key proteins and metabolites that may have a role in enhancing yield and biofortification. Plant growth-promoting bacteria (PGPB) and arbuscular mycorrhizal fungi (AMF) have an impact on grain yield and nutrition. This dynamic yet complex interaction implies a broad reprogramming of the plant's metabolic and proteomic activities. However, little information is available regarding the role of native PGPB and AMF and how they affect the plant proteome, especially under field conditions. Here, proteomic, protein-protein and protein-metabolite interaction studies in wheat triggered by PGPB, Bacillus subtilis CP4 either alone or together with AMF under field conditions was carried out. The dual inoculation with native PGPB (CP4) and AMF promoted the differential abundance of many proteins, such as histones, glutenin, avenin and ATP synthase compared to the control and single inoculation. Interaction study of these differentially expressed proteins using STRING revealed that they interact with other proteins involved in seed development and abiotic stress tolerance. Furthermore, these interacting proteins are involved in carbon fixation, sugar metabolism and biosynthesis of amino acids. Molecular docking predicted that wheat seed storage proteins, avenin and glutenin interact with secondary metabolites, such as trehalose, and sugars, such as xylitol. Mapping of differentially expressed proteins to KEGG pathways showed their involvement in sugar metabolism, biosynthesis of secondary metabolites and modulation of histones. These proteins and metabolites can serve as markers for improving wheat-PGPB-AMF interactions leading to higher yield and biofortification.

Study on the Mechanism of Astragalus Polysaccharide in Treating Pulmonary Fibrosis Based on "Drug-Target-Pathway" Network

Pulmonary fibrosis is a chronic, progressive and irreversible heterogeneous disease of pulmonary interstitial tissue. Its incidence is increasing year by year in the world, and it will be further increased due to the pandemic of COVID-19. However, at present, there is no safe and effective treatment for this disease, so it is very meaningful to find drugs with high efficiency and less adverse reactions. The natural astragalus polysaccharide has the pharmacological effect of anti-pulmonary fibrosis with little toxic and side effects. At present, the mechanism of anti-pulmonary fibrosis of astragalus polysaccharide is not clear. Based on the network pharmacology and molecular docking method, this study analyzes the mechanism of Astragalus polysaccharides in treating pulmonary fibrosis, which provides a theoretical basis for its further clinical application. The active components of Astragalus polysaccharides were screened out by Swisstarget database, and the related targets of pulmonary fibrosis were screened out by GeneCards database. Protein-protein interaction network analysis and molecular docking were carried out to verify the docking affinity of active ingredients. At present, through screening, we have obtained 92 potential targets of Astragalus polysaccharides for treating pulmonary fibrosis, including 11 core targets. Astragalus polysaccharides has the characteristics of multi-targets and multi-pathways, and its mechanism of action may be through regulating the expression of VCAM1, RELA, CDK2, JUN, CDK1, HSP90AA1, NOS2, SOD1, CASP3, AHSA1, PTGER3 and other genes during the development of pulmonary fibrosis.

The Binding of Alpinia galanga Oil and Its Nanoemulsion to Mammal GABAA Receptors Using Rat Cortical Membranes and an In Silico Modeling Platform

The anesthetic effect of Alpinia galanga oil (AGO) has been reported. However, knowledge of its pathway in mammals is limited. In the present study, the binding of AGO and its key compounds, methyl eugenol, 1,8-cineole, and 4-allylphenyl acetate, to gamma-aminobutyric acid type A (GABA_A) receptors in rat cortical membranes, was investigated using a [³H]muscimol binding assay and an in silico modeling platform. The results showed that only AGO and methyl eugenol displayed a positive modulation at the highest concentrations, whereas 1,8-cineole and 4-allylphenyl acetate were inactive. The result of AGO correlated well to the amount of methyl eugenol in AGO. Computational docking and dynamics simulations into the GABA_A receptor complex model (PDB: 6X3T) showed the stable structure of the GABA_A receptor–methyl eugenol complex with the lowest binding energy of −22.16 kcal/mol. This result shows that the anesthetic activity of AGO and methyl eugenol in mammals is associated with GABA_A receptor modulation. An oil-in-water nanoemulsion containing 20% w/w AGO (NE-AGO) was formulated. NE-AGO showed a significant increase in specific [³H]muscimol binding, to 179% of the control, with an EC₅₀ of 391 µg/mL. Intracellular studies show that normal human cells are highly tolerant to AGO and the nanoemulsion, indicating that NE-AGO may be useful for human anesthesia.

Enhancing drug-drug interaction prediction by three-way decision and knowledge graph embedding

Drug-Drug interaction (DDI) prediction is essential in pharmaceutical research and clinical application. Existing computational methods mainly extract data from multiple resources and treat it as binary classification. However, this cannot unambiguously tell the boundary between positive and negative samples owing to the incompleteness and uncertainty of derived data. A granular computing method called three-way decision is proved to be effective in making uncertain decision, but it relies on supplementary information to make delay decision. Recently, biomedical knowledge graph has been regarded as an important source to obtain abundant supplementary information about drugs. This paper proposes a three-way decision-based method called 3WDDI, in combination with knowledge graph embedding as supplementary features to enhance DDI prediction. The drug pairs are divided into positive, negative and boundary regions by Convolutional Neural Network (CNN) according to drug chemical structure feature. Further, delay decision is made for objects in the boundary region by integrating knowledge graph embedding feature to promote the accuracy of decision-making. The empirical results show that 3WDDI yields up to 0.8922, 0.9614, 0.9582, 0.8930 for Accuracy, AUPR, AUC and F1-score, respectively, and outperforms several baseline models.

Can Mesoporous Silica Speed Up Degradation of Benzodiazepines? Hints from Quantum Mechanical Investigations

This work reports for the first time a quantum mechanical study of the interactions of a model benzodiazepine drug, i.e., nitrazepam, with various models of amorphous silica surfaces, differing in structural and interface properties. The interest in these systems is related to the use of mesoporous silica as carrier in drug delivery. The adopted computational procedure has been chosen to investigate whether silica-drug interactions favor the drug degradation mechanism or not, hindering the beneficial pharmaceutical effect. Computed structural, energetics, and vibrational properties represent a relevant comparison for future experiments. Our simulations demonstrate that adsorption of nitrazepam on amorphous silica is a strongly exothermic process in which a partial proton transfer from the surface to the drug is observed, highlighting a possible catalytic role of silica in the degradation reaction of benzodiazepines.

Web Blog Content Curation Using Fuzzy-Related Capsule Network-Based Auto Encoder

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.

Computational identification of Plasmodium falciparum RNA pseudouridylate synthase as a viable drug target, its physicochemical properties, 3D structure prediction and prediction of potential inhibitors

The increased resistance to the currently effective antimalarial drugs against Plasmodium falciparum has necessitated the development of new drugs for malaria treatment. Many proteins have been predicted using various means as potential drug targets for the treatment of the P. falciparum malaria infection. Meanwhile, only a few studies went on to predict the 3-dimensional (3D) structure of potential target. Therefore, this study aimed to predict potential antimalarial drug targets against the deadliest malaria parasite P. falciparum as well as to determine the 3D structure and possible inhibitors of one of the targets. We employed machine learning approach to predict suitable drug targets in P. falciparum. Five of the predicted protein targets were considered as potential drug targets as they were non-homologous to their human counterparts. Out of these, we determined the physicochemical properties, predicted the 3D structure and carried out docking-based virtual screening of P. falciparum RNA pseudouridylate synthase, putative (PfRPuSP). The PfRPuSP was one of the potential five target proteins. Homology modelling and the ab initio methods were used to predict the 3D structure of PfRPuSP. Then, a compound library of 5621 molecules was constructed from PubChem and ChEMBL databases using 5-fluorouridine as the control inhibitor. Docking-based virtual screening was performed using Autodock 4.2 and Autodock Vina to select compounds with high binding affinity. A total of 11 compounds were selected based on their binding energies from 881 compounds which were manually examined after docking. Seven of the 11 compounds that exhibited remarkable interactions with the residues in the active sites of PfRPuSP were analysed. These compounds performed favourably when compared to the control inhibitor and predicted to bind better than 5-fluorouridine. These seven compounds are suggested as new potential lead structures for antimalarial treatment.

A computational biology approach for the identification of potential SARS-CoV-2 main protease inhibitors from natural essential oil compounds

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fomented a climate of fear worldwide due to its rapidly spreading nature, and high mortality rate. The World Health Organization (WHO) declared it as a global pandemic on 11th March, 2020. Many endeavors have been made to find appropriate medications to restrain the SARS CoV-2 infection from spreading but there is no specific antiviral therapy to date. However, a computer-aided drug design approach can be an alternative to identify probable drug candidates within a short time. SARS-CoV-2 main protease is a proven drug target, and it plays a pivotal role in viral replication and transcription. Methods: In this study, we identified a total of 114 essential oil compounds as a feasible anti-SARS-CoV-2 agent from several online reservoirs. These compounds were screened by incorporating ADMET profiling, molecular docking, and 50 ns of molecular dynamics simulation to identify potential drug candidates against the SARS-CoV-2 main protease. The crystallized SARS-CoV-2 main protease structure was collected from the RCSB PDB database (PDB ID 6LU7). Results: According to the results of the ADMET study, none of the compounds have any side effects that could reduce their druglikeness or pharmacokinetic properties. Out of 114 compounds, we selected bisabololoxide B, eremanthin, and leptospermone as our top drug candidates based on their higher binding affinity scores, and strong interaction with the Cys 145-His 41 catalytic dyad. Finally, the molecular dynamics simulation was implemented to evaluate the structural stability of the ligand-receptor complex. MD simulations disclosed that all the hits showed conformational stability compared to the positive control α-ketoamide. Conclusions: Our study showed that the top three hits might work as potential anti-SARS-CoV-2 agents, which can pave the way for discovering new drugs, but for experimental validation, they will require more in vivo trials.

A New World Monkey Resembles Human in Bitter Taste Receptor Evolution and Function via a Single Parallel Amino Acid Substitution

Bitter taste receptors serve as a vital component in the defense system against toxin intake by animals, and the family of genes encoding these receptors has been demonstrated, usually by family size variance, to correlate with dietary preference. However, few systematic studies of specific Tas2R to unveil their functional evolution have been conducted. Here, we surveyed Tas2R16 across all major clades of primates and reported a rare case of a convergent change to increase sensitivity to β-glucopyranosides in human and a New World monkey, the white-faced saki. Combining analyses at multiple levels, we demonstrate that a parallel amino acid substitution (K172N) shared by these two species is responsible for this functional convergence of Tas2R16. Considering the specialized feeding preference of the white-faced saki, the K172N change likely played an important adaptive role in its early evolution to avoid potentially toxic cyanogenic glycosides, as suggested for the human TAS2R16 gene.

Identification of FDA approved drugs against SARS-CoV-2 RNA dependent RNA polymerase (RdRp) and 3-chymotrypsin-like protease (3CLpro), drug repurposing approach

The RNA-dependent RNA polymerase (RdRp) and 3C-like protease (3CLpro) from SARS-CoV-2 play crucial roles in the viral life cycle and are considered the most promising targets for drug discovery against SARS-CoV-2. In this study, FDA-approved drugs were screened to identify the probable anti-RdRp and 3CLpro inhibitors by molecular docking approach. The number of ligands selected from the PubChem database of NCBI for screening was 1760. Ligands were energy minimized using Open Babel. The RdRp and 3CLpro protein sequences were retrieved from the NCBI database. For Homology Modeling predictions, we used the Swiss model server. Their structure was then energetically minimized using SPDB viewer software and visualized in the CHIMERA UCSF software. Molecular dockings were performed using AutoDock Vina, and candidate drugs were selected based on binding affinity (∆G). Hydrogen bonding and hydrophobic interactions between ligands and proteins were visualized using Ligplot and the Discovery Studio Visualizer v3.0 software. Our results showed 58 drugs against RdRp, which had binding energy of - 8.5 or less, and 69 drugs to inhibit the 3CLpro enzyme with a binding energy of - 8.1 or less. Six drugs based on binding energy and number of hydrogen bonds were chosen for the next step of molecular dynamics (MD) simulations to investigate drug-protein interactions (including Nilotinib, Imatinib and dihydroergotamine for 3clpro and Lapatinib, Dexasone and Relategravir for RdRp). Except for Lapatinib, other drugs-complexes were stable during MD simulation. Raltegravir, an anti-HIV drug, was observed to be the best compound against RdRp based on docking binding energy (-9.5 kcal/mole) and MD results. According to the MD results and binding energy, dihydroergotamine is a suitable candidate for 3clpro inhibition (-9.6 kcal/mol). These drugs were classified into several categories, including antiviral, antibacterial, anti-inflammatory, anti-allergic, cardiovascular, anticoagulant, BPH and impotence, antipsychotic, antimigraine, anticancer, and so on. The common prescription-indications for some of these medication categories appeared somewhat in line with manifestations of COVID-19. We hope that they can be beneficial for patients with certain specific symptoms of SARS-CoV-2 infection, but they can also probably inhibit viral enzymes. We recommend further experimental evaluations in vitro and in vivo on these FDA-approved drugs to assess their potential antiviral effect on SARS-CoV-2.

Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction

Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.

Critical assessment of AI in drug discovery

Introduction: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.Areas covered: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.Expert opinion: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.

Role of non-active site residues in maintaining New Delhi metallo-β-lactamase-1(NDM-1) function: an approach of site-directed mutagenesis and docking

New Delhi metallo-β-lactamase-1 (NDM-1) has been known to hydrolyze nearly all β-lactam antibiotics, leading to a multidrug-resistant state. Hence, it is important to study its structure and function in relation to controlling infections caused by such resistant bacterial strains. Mutagenesis is one of the approaches used to explore it. No study has been performed to explore the role of non-active site residues in the enzyme activity. This study includes mutations of three non-active site residues to comprehend its structure and function simultaneously. Three non-active site laboratory mutants of NDM-1 were generated by site-directed mutagenesis. The minimum inhibitory concentrations of cefotaxime, cefoxitin, imipenem and meropenem were reduced by up to 4-fold for these mutants compared with wild-type. The hydrolytic activity of mutants was also found to be reduced. Mutants showed a significant change in secondary structure compared with wild-type, as determined by CD spectrophotometry. The catalytic properties and stability of these mutants were found to be reduced. Hence, it revealed an imperative role of non-active site residues in the enzymatic activity of NDM-1.

m6A RNA Methylation Regulators Impact Prognosis and Tumor Microenvironment in Renal Papillary Cell Carcinoma

Accumulating evidence has proven that N6-methyladenosine (m⁶A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m⁶A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m⁶A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m⁶A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m⁶A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m⁶A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m⁶A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m⁶A RNA modification in PRCC and provide essential guidance for therapeutic strategies.

Identification of four key prognostic genes and three potential drugs in human papillomavirus negative head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a common tumor worldwide with poor prognosis. The pathogenesis of human papillomavirus (HPV)-positive and HPV-negative HNSCCs differs. However, few studies have considered the HPV status when identifying biomarkers for HNSCC. Thus, the identification of biomarkers for HPV-positive and HPV-negative HNSCCs is urgently needed.

METHODS

Three microarray datasets from Gene Expression Omnibus (GEO) were analyzed, and the differentially expressed genes (DEGs) were obtained. Then, functional enrichment pathway analysis was performed and protein-protein interaction (PPI) networks were constructed. The expression of hub genes at both the mRNA and protein level was determined in Oncomine, The Cancer Genome Atlas (TCGA) and the Human Protein Atlas (HPA). In addition, survival analysis of the patient stratified by HPV status and the expression levels of key genes were performed based on TCGA data. The role of AREG, STAG3, CAV1 and C19orf57 in cancer were analyzed through Gene set enrichment analysis (GSEA). The top ten small molecule drugs were identified and the therapeutic value of zonisamide, NVP-AUY922, PP-2 and fostamatinib was further evaluated in six HPV-negative HNSCC cell lines. Finally, the therapeutic value of NVP-AUY922 was tested in vivo based on three HPV-negative HNSCC models, and statistical analysis was performed.

RESULTS

In total, 47 DEGs were obtained, 11 of which were identified as hub genes. Biological process analysis indicated that the hub genes were associated with the G1/S transition of the mitotic cell cycle. Survival analysis uncovered that the prognostic value of AREG, STAG3, C19orf57 and CAV1 differed between HPV-positive and HPV-negative patients. Gene set enrichment analysis (GSEA) showed the role of AREG, STAG3 and CAV1 in dysregulated pathways of tumor. Ten small molecules were identified as potential drugs specifically for HPV-positive or HPV-negative patients; three-NVP-AUY922, fostamatinib and PP-2-greatly inhibited the proliferation of six HPV-negative HNSCC cell lines in vitro, and NVP-AUY922 inhibited three HPV-negative HNSCC xenografts in vivo.

CONCLUSIONS

In conclusion, AREG, STAG3, C19orf57 and CAV1 are key prognostic factors and potential therapeutic targets in HPV-negative HNSCC. NVP-AUY922, fostamatinib and PP-2 may be effective drugs for HPV-negative HNSCC.

Tentative Peptide‒Lipid Bilayer Models Elucidating Molecular Behaviors and Interactions Driving Passive Cellular Uptake of Collagen-Derived Small Peptides

Collagen contains hydroxyproline (Hyp), which is a unique amino acid. Three collagen-derived small peptides (Gly-Pro-Hyp, Pro-Hyp, and Gly-Hyp) interacting across a lipid bilayer (POPC model membrane) for cellular uptakes of these collagen-derived small peptides were studied using accelerated molecular dynamics simulation. The ligands were investigated for their binding modes, hydrogen bonds in each coordinate frame, and mean square displacement (MSD) in the Z direction. The lipid bilayers were evaluated for mass and electron density profiles of the lipid molecules, surface area of the head groups, and root mean square deviation (RMSD). The simulation results show that hydrogen bonding between the small collagen peptides and plasma membrane plays a significant role in their internalization. The translocation of the small collagen peptides across the cell membranes was shown. Pro-Hyp laterally condensed the membrane, resulting in an increase in the bilayer thickness and rigidity. Perception regarding molecular behaviors of collagen-derived peptides within the cell membrane, including their interactions, provides the novel design of specific bioactive collagen peptides for their applications.

Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods.

Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the scope of a single Account. In this Account, we will focus on two key areas where deep learning has impacted molecular design: the prediction of molecular properties and the de novo generation of suggestions for new molecules.One of the most significant advances in the development of quantitative structure-activity relationships (QSARs) has come from the application of deep learning methods to the prediction of the biological activity and physical properties of molecules in drug discovery programs. Rather than employing the expert-derived chemical features typically used to build predictive models, researchers are now using deep learning to develop novel molecular representations. These representations, coupled with the ability of deep neural networks to uncover complex, nonlinear relationships, have led to state-of-the-art performance. While deep learning has changed the way that many researchers approach QSARs, it is not a panacea. As with any other machine learning task, the design of predictive models is dependent on the quality, quantity, and relevance of available data. Seemingly fundamental issues, such as optimal methods for creating a training set, are still open questions for the field. Another critical area that is still the subject of multiple research efforts is the development of methods for assessing the confidence in a model.Deep learning has also contributed to a renaissance in the application of de novo molecule generation. Rather than relying on manually defined heuristics, deep learning methods learn to generate new molecules based on sets of existing molecules. Techniques that were originally developed for areas such as image generation and language translation have been adapted to the generation of molecules. These deep learning methods have been coupled with the predictive models described above and are being used to generate new molecules with specific predicted biological activity profiles. While these generative algorithms appear promising, there have been only a few reports on the synthesis and testing of molecules based on designs proposed by generative models. The evaluation of the diversity, quality, and ultimate value of molecules produced by generative models is still an open question. While the field has produced a number of benchmarks, it has yet to agree on how one should ultimately assess molecules "invented" by an algorithm.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2-P3 α-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach

The SARS-CoV-2 main protease (M^pro) is an attractive target towards discovery of drugs to treat COVID-19 because of its key role in virus replication. The atomic structure of M^pro in complex with an α-ketoamide inhibitor (Lig13b) is available (PDB ID:6Y2G). Using 6Y2G and the prior knowledge that protease inhibitors could eradicate COVID-19, we designed a computational study aimed at identifying FDA-approved drugs that could interact with M^pro. We searched the DrugBank and PubChem for analogs and built a virtual library containing ∼33,000 conformers. Using high-throughput virtual screening and ligand docking, we identified Isavuconazonium, a ketoamide inhibitor (α-KI) and Pentagastrin as the top three molecules (Lig13b as the benchmark) based on docking energy. The ΔG_bind of Lig13b, Isavuconazonium, α-KI, Pentagastrin was −28.1, −45.7, −44.7, −34.8 kcal/mol, respectively. Molecular dynamics simulation revealed that these ligands are stable within the M^pro active site. Binding of these ligands is driven by a variety of non-bonded interaction, including polar bonds, H-bonds, van der Waals and salt bridges. The overall conformational dynamics of the complexed-M^pro was slightly altered relative to apo-M^pro. This study demonstrates that three distinct classes molecules, Isavuconazonium (triazole), α-KI (ketoamide) and Pentagastrin (peptide) could serve as potential drugs to treat patients with COVID-19.

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Using computational modelling, we show that SARS-CoV-2 main protease (M^pro) interacts with peptidomimetic drugs.

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The interaction between the M^pro and the peptidomimetics is energetically favourable.

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Isavuconazonium, a P2–P3 α-ketoamide derivative and Pentagastrin showed the tightest and most favourable binding to M^pro.

In silico drug design and molecular docking studies targeting Akt1 (RAC-alpha serine/threonine-protein kinase) and Akt2 (RAC-beta serine/threonine-protein kinase) proteins and investigation of CYP (cytochrome P450) inhibitors against MAOB (monoamine oxidase B) for OSCC (oral squamous cell carcinoma) treatment

The overexpression of Akt1 (RAC-alpha serine/threonine-protein Kinase) and Akt2 (RAC-beta serine/threonine-protein Kinase) is a hallmark of Oral Squamous Cell Carcinoma (OSCC). Because of the elevated frequency of OSCC occurrence in South Asian countries, novel therapeutic approaches are indispensable. Drugs that inhibit the overexpression of Akt1 and Akt2 proteins in Akt pathway and do not cause reduced expression of MAOB can be leads for OSCC treatment. In this study, Akt1, Akt2 and MAOB were targeted and 100 CYP inhibitors were screened through several in silico approaches and Galuteolin and Linarin were identified as potential leads for OSCC treatment as they inhibited Akt1 proteins with strong binding affinities of -12.3 and -11.5 kcal/mol respectively and also Akt2 proteins with strong binding affinities of -11.4 and -11.1 kcal/mol respectively, but they did not inhibit MAOB. Decreased expression of MAOB in tissues causes OSCC but overexpression is also responsible for other types of diseases and cancers. From the investigation of CYP inhibitors against MAOB, five CYP inhibitors- Diosmetin, Acacetin, Epicatechin, Eriodictyol and Capillin have expressed inhibitory action against MAOB without any interference with Akt1 and Akt2. This study mainly represents that Galuteolin and Linarin in the Akt pathway can be perceived for OSCC treatment and other five CYP inhibitors - Diosmetin, Acacetin, Epicatechin, Eriodictyol and Capillin for the treatment of other diseases and cancers caused by overexpression of MAOB. ADMET properties of CYP inhibitors obtained from admetSAR 2.0 and were compared with reference drugs for validation. Communicated by Ramaswamy H. Sarma.

Plant-Based Phytochemicals as Possible Alternative to Antibiotics in Combating Bacterial Drug Resistance

The unprecedented use of antibiotics that led to development of resistance affect human health worldwide. Prescription of antibiotics imprudently and irrationally in different diseases progressed with the acquisition and as such development of antibiotic resistant microbes that led to the resurgence of pathogenic strains harboring enhanced armors against existing therapeutics. Compromised the treatment regime of a broad range of antibiotics, rise in resistance has threatened human health and increased the treatment cost of diseases. Diverse on metabolic, genetic and physiological fronts, rapid progression of resistant microbes and the lack of a strategic management plan have led researchers to consider plant-derived substances (PDS) as alternative or in complementing antibiotics against the diseases. Considering the quantitative characteristics of plant constituents that attribute health beneficial effects, analytical procedures for their isolation, characterization and phytochemical testing for elucidating ethnopharmacological effects has being worked out for employment in the treatment of different diseases. With an immense potential to combat bacterial infections, PDSs such as polyphenols, alkaloids and tannins, present a great potential for use, either as antimicrobials or as antibiotic resistance modifiers. The present study focuses on the mechanisms by which PDSs help overcome the surge in resistance, approaches for screening different phytochemicals, methods employed in the identification of bioactive components and their testing and strategies that could be adopted for counteracting the lethal consequences of multidrug resistance.

Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications

The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure-activity relationships (QSAR)-often as its 3D QSAR variant-are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment

All drugs usually have side effects, which endanger the health of patients. To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming. So, computation-based methods have been developed to accurately and quickly predict side effects. To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model. TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA). LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data. To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively. Compared with other methods, our model achieves better performance on three benchmark datasets. The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.

A multimodal deep learning framework for predicting drug–drug interaction events

Motivation

Drug–drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions.

Results

In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug–drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision–recall curve of 0.9208.

Availability and implementation

The source code and data are available at https://github.com/YifanDengWHU/DDIMDL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of DOT1L inhibitors by structure-based virtual screening adapted from a nucleoside-focused library

Disruptor of Telomeric Silencing 1-Like (DOT1L), the sole histone H3 lysine 79 (H3K79) methyltransferase, is required for leukemogenic transformation in a subset of leukemias bearing chromosomal translocations of the Mixed Lineage Leukemia (MLL) gene, as well as other cancers. Thus, DOT1L is an attractive therapeutic target and discovery of small molecule inhibitors remain of high interest. Herein, we are presenting screening results for a unique focused library of 1200 nucleoside analogs originally produced under the aegis of the NIH Pilot Scale Library Program. The complete nucleoside set was screened virtually against DOT1L, resulting in 210 putative hits. In vitro screening of the virtual hits resulted in validation of 11 compounds as DOT1L inhibitors clustered into two distinct chemical classes, adenosine-based inhibitors and a new chemotype that lacks adenosine. Based on the developed DOT1L ligand binding model, a structure-based design strategy was applied and a second-generation of non-nucleoside DOT1L inhibitors was developed. Newly synthesized compound 25 was the most potent DOT1L inhibitor in the new series with an IC₅₀ of 1.0 μM, showing 40-fold improvement in comparison with hit 9 and exhibiting reasonable on target effects in a DOT1L dependent murine cell line. These compounds represent novel chemical probes with a unique non-nucleoside scaffold that bind and compete with the SAM binding site of DOT1L, thus providing foundation for further medicinal chemistry efforts to develop more potent compounds.

A Bayesian machine learning approach for drug target identification using diverse data types

Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.

Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.