Large-scale prediction and testing of drug activity on side-effect targets

Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps

Food compounds and their molecular interactions are crucial for health and provide new chemotypes and targets for drug and nutraceutic design. Here, we retrieve and analyze the complete set of published interactions of food compounds with human proteins using the FooDB as a compound set and ChEMBL as a source of interactions. The data are analyzed in terms of 19 target classes and 19 compound classes, showing a small fraction of target assignment for the compounds (1.6%) and unraveling multiple gaps in the chemobiological space for these molecules. By using well-established cheminformatic approaches [similarity ensemble approach (SEA) combined with the maximum Tanimoto coefficient to the nearest bioactive, "SEA + TC"], we achieve a much enhanced target assignment (64.2%), filling many of the gaps with target hypothesis for fast focused testing. By publishing these data sets and analyses, we expect to provide a set of resources to speed up the full clarification of the chemobiological space of food compounds, opening new opportunities for drug and nutraceutic design.

Advances in delivery methods of Arthrospira platensis (spirulina) for enhanced therapeutic outcomes

Arthrospira platensis (A. platensis) aqueous extract has massive amounts of natural products that can be used as future drugs, such as C-phycocyanin, allophycocyanin, etc. This extract was chosen because of its high adaptability, which reflects its resolute genetic composition. The proactive roles of cyanobacteria, particularly in the medical field, have been discussed in this review, including the history, previous food and drug administration (FDA) reports, health benefits and the various dose-dependent therapeutic functions that A. platensis possesses, including its role in fighting against lethal diseases such as cancer, SARS-CoV-2/COVID-19, etc. However, the remedy will not present its maximal effect without the proper delivery to the targeted place for deposition. The goal of this research is to maximize the bioavailability and delivery efficiency of A. platensis constituents through selected sites for effective therapeutic outcomes. The solutions reviewed are mainly on parenteral and tablet formulations. Moreover, suggested enteric polymers were discussed with minor composition variations applied for better storage in high humid countries alongside minor variations in the polymer design were suggested to enhance the premature release hindrance of basic drugs in low pH environments. In addition, it will open doors for research in delivering active pharmaceutical ingredients (APIs) in femtoscale with the use of various existing and new formulations.Abbrevations: SDGs; Sustainable Development Goals, IL-4; Interleukin-4, HDL; High-Density Lipoprotein, LDL; Low-Density Lipoprotein, VLDL; Very Low-Density Lipoprotein, C-PC; C-Phycocyanin, APC; Allophycocyanin, PE; Phycoerythrin, COX-2; Cyclooxygenase-2, RCTs; Randomized Control Trials, TNF-α; Tumour Necrosis Factor-alpha, γ-LFA; Gamma-Linolenic Fatty Acid, PGs; Polyglycans, PUFAs: Polyunsaturated Fatty Acids, NK-cell; Natural Killer Cell, FDA; Food and Drug Administration, GRAS; Generally Recognized as Safe, SD; Standard Deviation, API; Active Pharmaceutical Ingredient, DW; Dry Weight, IM; Intramuscular, IV; Intravenous, ID; Intradermal, SC; Subcutaneous, AERs; Adverse Event Reports, DSI-EC; Dietary Supplement Information Executive Committee, cGMP; Current Good Manufacturing Process, A. platensis; Arthrospira platensis, A. maxima; Arthrospira maxima, Spirulina sp.; Spirulina species, Arthrospira; Spirulina, Tecuitlatl; Spirulina, CRC; Colorectal Cancer, HDI; Human Development Index, Tf; Transferrin, TfR; Transferrin Receptor, FR; Flow Rate, CPP; Cell Penetrating Peptide, SUV; Small Unilamenar Vesicle, LUV; Large Unilamenar Vesicle, GUV; Giant Unilamenar Vesicle, MLV; Multilamenar Vesicle, COVID-19; Coronavirus-19, PEGylated; Stealth, PEG; Polyethylene Glycol, OSCEs; Objective Structured Clinical Examinations, GI; Gastrointestinal Tract, CAP; Cellulose Acetate Phthalate, HPMCP, Hydroxypropyl Methyl-Cellulose Phthalate, SR; Sustained Release, DR; Delay Release, Poly(MA-EA); Polymethyl Acrylic Co-Ethyl Acrylate, f-DR L-30 D-55; Femto-Delay Release Methyl Acrylic Acid Co-Ethyl Acrylate Polymer, MW; Molecular Weight, Tg; Glass Transition Temperature, SN2; Nucleophilic Substitution 2, EPR; Enhance Permeability and Retention, VEGF; Vascular Endothelial Growth Factor, RGD; Arginine-Glycine-Aspartic Acid, VCAM-1; Vascular Cell Adhesion Molecule-1, P; Coefficient of Permeability, PES; Polyether Sulfone, pHe; Extracellular pH, ζ-potential; Zeta potential, NTA; Nanoparticle Tracking Analysis, PB; Phosphate Buffer, DLS; Dynamic Light Scattering, AFM; Atomic Force Microscope, Log P; Partition Coefficient, MR; Molar Refractivity, tPSA; Topological Polar Surface Area, C log P; Calculated Partition Coefficient, CMR; Calculated Molar Refractivity, Log S; Solubility Coefficient, pka; Acid Dissociation Constant, DDAB; Dimethyl Dioctadecyl Ammonium Bromide, DOPE; Dioleoylphosphatidylethanolamine, GDP; Good Distribution Practice, RES; Reticuloendothelial System, PKU; Phenylketonuria, MS; Multiple Sclerosis, SLE; Systemic Lupus Erythematous, NASA; National Aeronautics and Space Administration, DOX; Doxorubicin, ADRs; Adverse Drug Reactions, SVM; Support Vector Machine, MDA; Malondialdehyde, TBARS; Thiobarbituric Acid Reactive Substances, CRP; C-Reactive Protein, CK; Creatine Kinase, LDH; Lactated Dehydrogenase, T2D; Type 2 Diabetes, PCB; Phycocyanobilin, PBP; Phycobiliproteins, PEB; Phycoerythrobilin, DPP-4; Dipeptidyl Peptidase-4, MTT; 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide, IL-2; Interleukin-2, IL-6; Interleukin-6, PRISMA; Preferred Reporting Items for Systematic Reviews and Meta-Analyses, STATA; Statistics, HepG2; Hepatoblastoma, HCT116; Colon Cancer Carcinoma, Kasumi-1; Acute Leukaemia, K562; Chronic Leukaemia, Se-PC; Selenium-Phycocyanin, MCF-7; Breast Cancer Adenocarcinoma, A375; Human Melanoma, RAS; Renin-Angiotensin System, IQP; Ile-Gln-Pro, VEP; Val-Glu-Pro, Mpro; Main Protease, PLpro; Papin-Like Protease, BMI; Body Mass Index, IC50; Inhibitory Concentration by 50%, LD50; Lethal Dose by 50%, PC12 Adh; Rat Pheochromocytoma Cells, RNS; Reactive Nitrogen Species, Hb1Ac; hemoglobin A1c.

Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities

As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.

Yes SIR! On the structure-inactivity relationships in drug discovery

In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.

In silico investigation of phytoconstituents from Cameroonian medicinal plants towards COVID-19 treatment

In silico studies performed on the metabolites of four Cameroonian medicinal plants with a view to propose potential molecules to fight against COVID-19 were carried out. At first, molecular docking was performed for a set of 84 selected phytochemicals with SARS-CoV-2 main protease (PDB ID: 6lu7) protein. It was further followed by assessing the pharmacokinetics and pharmacological abilities of 15 compounds, which showed low binding energy values. As the screening criteria for their ADMET properties were performed, only two compounds have shown suitable pharmacological properties for human administration which were shortlisted. Furthermore, the stability of binding of these compounds was assessed by performing molecular dynamics (MD) simulations. Based on further analysis through molecular dynamics simulations and reactivity studies, it was concluded that only the Pycnanthuquinone C (17) and the Pycnanthuquinone A (18) extracted from the Pycnanthus angolensis could be considered as candidate inhibitors for targeted protein. Indeed, we expect that these compounds could show excellent in vitro and in vivo activity against SARS-CoV-2.

SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI's deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.

Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.

In silico drug repositioning based on integrated drug targets and canonical correlation analysis

Background

Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level.

Methods

Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug–target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug’s mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets.

Results

We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions.

Conclusions

To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01203-1.

Structure-guided engineering of tick evasins for targeting chemokines in inflammatory diseases

Inflammatory diseases collectively account for numerous deaths and morbidity worldwide. New treatment approaches are needed. A central feature of inflammatory diseases is the recruitment of leukocytes to the affected tissues, which is stimulated by secreted proteins called chemokines. Effective suppression of leukocyte recruitment could be achieved by simultaneously targeting multiple chemokines, a natural molecular strategy used by tick salivary proteins called evasins. Here, we describe the structural and molecular features of a tick evasin that control its ability to bind and block a limited set of chemokines. By modifying these features, we demonstrate that evasins can be engineered to alter the array of chemokines that they target. Thus, this study establishes a structure-based paradigm for the development of antiinflammatory therapeutics.

As natural chemokine inhibitors, evasin proteins produced in tick saliva are potential therapeutic agents for numerous inflammatory diseases. Engineering evasins to block the desired chemokines and avoid off-target side effects requires structural understanding of their target selectivity. Structures of the class A evasin EVA-P974 bound to human CC chemokine ligands 7 and 17 (CCL7 and CCL17) and to a CCL8-CCL7 chimera reveal that the specificity of class A evasins for chemokines of the CC subfamily is defined by conserved, rigid backbone–backbone interactions, whereas the preference for a subset of CC chemokines is controlled by side-chain interactions at four hotspots in flexible structural elements. Hotspot mutations alter target preference, enabling inhibition of selected chemokines. The structure of an engineered EVA-P974 bound to CCL2 reveals an underlying molecular mechanism of EVA-P974 target preference. These results provide a structure-based framework for engineering evasins as targeted antiinflammatory therapeutics.

Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments

A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.

A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals

To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future.

To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from both molecule structure and biomedical text information, the authors propose a machine reading system that bridges both types of information.

Picture-word order compound protein interaction: Predicting compound-protein interaction using structural images of compounds

Identifying potential associations between proteins and compounds is significant and challenging in the drug discovery process. Existing deep-learning-based methods tend to treat compounds and proteins as sequences or graphs. Inspired by the rapid development of computer vision technology, we argue that more abundant characterizations can be extracted from the images of compounds than from their sequences or graphs. Therefore, we propose an interaction model named picture-word order compound protein interaction (PWO-CPI) which learns the representation from structural images of compounds and protein sequences by using convolutional neural network (CNN). The experiments show that PWO-CPI outperforms state-of-the-art CPI prediction models. We also perform drug-drug interaction (DDI) experiments to validate the strong potential of structural formula images of molecular structures as molecular features. In addition, with the aid of generative adversarial networks, the visualization of image features demonstrates PWO-CPI can learn compound structural features implicitly and automatically.

An AI-based prediction model for drug-drug interactions in osteoporosis and Paget's diseases from SMILES

Referring to common skeletal-related diseases, osteoporosis and Paget's are two of the most frequently found diseases in the elderly. Nowadays, the combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which contains various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, furthermore, to mitigate cost and time in implementing the best combination of medications in clinical practice. Our dataset was collected from the DrugBank database, and we then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms have been implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74% in predicting DDIs. It was superior to individual models and previous methods in most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.

Polypharmacology: The science of multi-targeting molecules

Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.

MDL-CPI: multi-view deep learning model for compound-protein interaction prediction

Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug discovery and development. Many computational efforts have been done to accelerate the development of this field. However, the current predictive performance is still not satisfactory, and existing methods consider only protein and compound features, ignoring their interactive information. In this study, we propose a multi-view deep learning method named MDL-CPI for CPI prediction. To sufficiently extract discriminative information, we introduce a hybrid architecture that leverages BERT (Bidirectional Encoder Representations from Transformers) and CNN (Convolutional Neural Network) to extract protein features from a sequential perspective, use the GNN (Graph Neural Networks) to extract compound features from a structural perspective, and generate a unified feature space by using AE2 (Autoencoder in Autoencoder Networks) network to learn the interactive information between BERT-CNN and Graph embeddings. Comparative results on benchmark datasets show that our proposed method exhibits better performance compared to existing CPI prediction methods, demonstrating the strong predictive ability of our model. Importantly, we demonstrate that the learned interactive information between compounds and proteins is critical to improve predictive performance. We release our source code and dataset at: https://github.com/Longwt123/MDL-CPI.

AI-powered drug repurposing for developing COVID-19 treatments

Emerging infectious diseases are an ever-present threat to public health, and COVID-19 is the most recent example. There is an urgent need to develop a robust framework to combat the disease with safe and effective therapeutic options. Compared to de novo drug discovery, drug repurposing may offer a lower-cost and faster drug discovery paradigm to explore potential treatment options of existing drugs. This chapter elucidates the advantages of artificial intelligence (AI) in enhancing the drug repurposing process from a data science perspective, using COVID-19 as an example. First, we elaborate on how AI-powered drug repurposing benefits from the accumulated data and knowledge of COVID-19 natural history and pathogenesis. Second, we summarize the pros and cons of AI-powered drug repurposing strategies to facilitate fit-for-purpose selection. Finally, we outline challenges of AI-powered drug repurposing from a regulatory perspective and suggest some potential solutions.

•

AI-powered drug purposing is promising for emerging treatments for COVID-19 infection.

•

Accumulated biological data profiles facilitate AI-based drug repurposing efforts for development of COVID-19 therapies.

•

The ‘fit-for-purpose selection of AI-powered drug repurposing strategies is key to uncovering hidden information among drugs, targets, and diseases.

•

Efforts from different stakeholders boost the adoption of AI-powered drug repurposing in the regulatory setting.

SPP-CPI: Predicting Compound-Protein Interactions Based On Neural Networks

Identifying interactions between compound and protein is a substantial part of the drug discovery process. Accurate prediction of interaction relationships can greatly reduce the time of drug development. The uniqueness of our method lies in three aspects:1) it represents a compound with a distance matrix. A distance matrix can capture the structural information, compared with the SMILES string. On the other hand, a distance matrix does not require complex data preprocessing for the molecular structure as the molecular graph representation, and is easier to obtain; 2) it uses SPP(Spatial pyramid pooling)-net to extract compound features, which has been successfully applied in image classification; and 3) it extracts protein features through the natural language processing method (doc2vec) to obtain sequence semantic information. We evaluated our method on three benchmark datasets-human, C.elegans, and DUDE-and the experimental results demonstrate that our proposed model presents competitive performance against state-of-the-art predictors. We also carried out drug-drug interaction (DDI) experiments to verify the strong potential of distance matrix as molecular characteristics. The source code and datasets are available at https://github.com/lxlsu/SPP_CPI.

Antifungal Activity of N-(4-Halobenzyl)amides against Candida spp. and Molecular Modeling Studies

Fungal infections remain a high-incidence worldwide health problem that is aggravated by limited therapeutic options and the emergence of drug-resistant strains. Cinnamic and benzoic acid amides have previously shown bioactivity against different species belonging to the Candida genus. Here, 20 cinnamic and benzoic acid amides were synthesized and tested for inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019. Five compounds inhibited the Candida strains tested, with compound 16 (MIC = 7.8 µg/mL) producing stronger antifungal activity than fluconazole (MIC = 16 µg/mL) against C. krusei ATCC 14243. It was also tested against eight Candida strains, including five clinical strains resistant to fluconazole, and showed an inhibitory effect against all strains tested (MIC = 85.3–341.3 µg/mL). The MIC value against C. krusei ATCC 6258 was 85.3 mcg/mL, while against C. krusei ATCC 14243, it was 10.9 times smaller. This strain had greater sensitivity to the antifungal action of compound 16. The inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019 was also achieved by compounds 2, 9, 12, 14 and 15. Computational experiments combining target fishing, molecular docking and molecular dynamics simulations were performed to study the potential mechanism of action of compound 16 against C. krusei. From these, a multi-target mechanism of action is proposed for this compound that involves proteins related to critical cellular processes such as the redox balance, kinases-mediated signaling, protein folding and cell wall synthesis. The modeling results might guide future experiments focusing on the wet-lab investigation of the mechanism of action of this series of compounds, as well as on the optimization of their inhibitory potency.

Analysis of anticholinergic adverse effects using two large databases: The US Food and Drug Administration Adverse Event Reporting System database and the Japanese Adverse Drug Event Report database

INTRODUCTION

Anticholinergic adverse effects (AEs) are a problem for elderly people. This study aimed to answer the following questions. First, is an analysis of anticholinergic AEs using spontaneous adverse drug event databases possible? Second, what is the main drug suspected of inducing anticholinergic AEs in the databases? Third, do database differences yield different results?

METHODS

We used two databases: the US Food and Drug Administration Adverse Event Reporting System database (FAERS) and the Japanese Adverse Drug Event Report database (JADER) recorded from 2004 to 2020. We defined three types of anticholinergic AEs: central nervous system (CNS) AEs, peripheral nervous system (PNS) AEs, and a combination of these AEs. We counted the number of cases and evaluated the ratio of drug-anticholinergic AE pairs between FAERS and JADER. We computed reporting odds ratios (RORs) and assessed the drugs using Beers Criteria®.

RESULTS

Constipation was the most reported AE in FAERS. The ratio of drug-anticholinergic AE pairs was statistically significantly larger in FAERS than JADER. Overactive bladder agents were suspected drugs common to both databases. Other drugs differed between the two databases. CNS AEs were associated with antidementia drugs in FAERS and opioids in JADER. In the assessment using Beers Criteria®, signals were detected for almost all drugs. Between the two databases, a significantly higher positive correlation was observed for PNS AEs (correlation coefficient 0.85, P = 0.0001). The ROR was significantly greater in JADER.

CONCLUSIONS

There are many methods to investigate AEs. This study shows that the analysis of anticholinergic AEs using spontaneous adverse drug event databases is possible. From this analysis, various suspected drugs were detected. In particular, FAERS had many cases. The differences in the results between the two databases may reflect differences in the reporting countries. Further study of the relationship between drugs and CNS AEs should be conducted.

Repurposable drugs for SARS-CoV-2 and influenza sepsis with scRNA-seq data targeting post-transcription modifications

Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. The lack of time for new drug discovery and the urgent need for rapid disease control to reduce mortality have led to a search for quick and effective alternatives to novel therapeutics, for example drug repurposing. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from patients with mild and severe COVID-19 (GEO: GSE145926, public data available and accessed on 22 April 2020). We identified 281 FDA-approved drugs that have the potential to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested and demonstrated the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a, two chemical inhibitors of glycosylation (a post-translational modification) on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus as well as on the transcription and translation of host cell cytokines and their regulators (IFNs and ISGs). In conclusion, we have identified and experimentally validated repurposable anti-SARS-CoV-2 and IAV drugs using a systems biology approach, which may have the potential for treating these viral infections and their complications (sepsis).

Quasi-Supervised Strategies for Compound-Protein Interaction Prediction

In-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology

In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A₁AR and A_2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.

Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

Evolution of kinase polypharmacology across HSP90 drug discovery

Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to identify and systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared with other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimization to discover luminespib and that the hit, leads, and clinical candidate all have different polypharmacological profiles. We therefore recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.

Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach

Psychostimulant drugs, such as cocaine, inhibit dopamine reuptake via blockading the dopamine transporter (DAT), which is the primary mechanism underpinning their abuse. Atypical DAT inhibitors are dissimilar to cocaine and can block cocaine- or methamphetamine-induced behaviors, supporting their development as part of a treatment regimen for psychostimulant use disorders. When developing these atypical DAT inhibitors as medications, it is necessary to avoid off-target binding that can produce unwanted side effects or toxicities. In particular, the blockade of a potassium channel, human ether-a-go-go (hERG), can lead to potentially lethal ventricular tachycardia. In this study, we established a counter screening platform for DAT and against hERG binding by combining machine learning-based quantitative structure-activity relationship (QSAR) modeling, experimental validation, and molecular modeling and simulations. Our results show that the available data are adequate to establish robust QSAR models, as validated by chemical synthesis and pharmacological evaluation of a validation set of DAT inhibitors. Furthermore, the QSAR models based on subsets of the data according to experimental approaches used have predictive power as well, which opens the door to target specific functional states of a protein. Complementarily, our molecular modeling and simulations identified the structural elements responsible for a pair of DAT inhibitors having opposite binding affinity trends at DAT and hERG, which can be leveraged for rational optimization of lead atypical DAT inhibitors with desired pharmacological properties.

Validation strategies for target prediction methods

Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.

Ab Initio Machine Learning in Chemical Compound Space

Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions

Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the laborious, costly, and time-consuming traditional wet-lab methods. Most proposed methods focus on separated drug and target descriptors, calculated, respectively, from chemical structures and protein sequences, and fail to introduce and extract features where the interaction information is embedded. In this paper, we propose a new three-step method based on matrix factorisation and generative adversarial network (GAN) for drug-target interaction prediction. Firstly, the matrix factorisation technique is used to capture and extract the joint interaction feature, for both drugs and targets, from the drug-target interaction matrix. Then, a GAN is introduced for data augmentation. It generates a fake positive sample similar to the real positive sample (known interactions) in order to balance the samples, allow the exploitation of the entire negative sample, and increase the data size for an accurate prediction. Finally, a fully connected four-layer neural network is built for classification. Experimental results illustrate a higher prediction performance of the proposed method compared to shallow classifiers and to state-of-the-art methods with an accuracy higher than 97%. Moreover, the data generation effect is confirmed by evaluating the proposed method with and without the generation step. These results demonstrated the efficiency of the latent interaction features and data generation on predicting new drugs or repurposing existing drugs. Overview of the WGANMF-DTI workflow for the Drug-Target Interaction Prediction task.

Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning

Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in silico can thereby facilitate drug discovery as well as drug repositioning. Various machine learning models have been developed over the years to predict such interactions. Multi-output learning models in particular have drawn the attention of the scientific community due to their high predictive performance and computational efficiency. These models are based on the assumption that all the labels are correlated with each other. However, this assumption is too optimistic. Here, we address drug-target interaction prediction as a multi-label classification task that is combined with label partitioning. We show that building multi-output learning models over groups (clusters) of labels often leads to superior results. The performed experiments confirm the efficiency of the proposed framework.

Versatile gadolinium(III)-phthalocyaninate photoagent for MR/PA imaging-guided parallel photocavitation and photodynamic oxidation at single-laser irradiation

Current light-mediated photodynamic therapy (PDT) is far underutilized in clinical cancer treatment due to its low pharmacological effect. We herein proposed a new gadolinium(III)-phthalocyanine (GdPc)-enabled phototherapeutics, photoacoustic/dynamic therapy (PADT), towards in vivo solid tumors via parallel-produced photocavitation and photodynamic oxidation with excitation by a single pulsed laser. We demonstrated that pulsed irradiation of GdPc could simultaneously produce an intense acoustic effect and a high-level ¹O₂ quantum yield to afford mitochondrial damage and initiate programmed cell death. Under the guidance of magnetic resonance/photoacoustic dual-modal imaging, the mechanical oxygen-independent destruction of acoustic cavitation and the chemical damage of ¹O₂ were validated to afford combinatorial inhibition of tumors under either normal or hypoxic conditions after the agent delivered into the cancer cells by a pH-sensitive nanomicelle. The single-laser initiated PADT using GdPc as a versatile photoagent maximizes the use of light energy to minimize the dose requirement of oxygen and agent towards high therapeutic efficacy, surpassing dramatically over conventional PDT.

Predicting biological pathways of chemical compounds with a profile-inspired approach

Background

Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures.

Results

We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74–0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway.

Conclusions

The method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04252-y.

Potential Biological Targets Prediction, ADME Profiling, & Molecular Docking studies of Novel Steroidal Products from Cunninghamella Blakesleana

BACKGROUND

New potential biological targets prediction through inverse molecular docking technique is an another smart strategy to forecast the possibility of compounds being biologically active against various target receptors.

OBJECTIVES

In this case of designed study, we screened our recently obtained novel acetylinic steroidal biotransformed products [(1) 8-β-methyl-14-α-hydroxy∆4tibolone (2) 9-α-Hydroxy∆4 tibolone (3) 8-β-methyl-11-β-hydroxy∆4tibolone (4) 6-β-hydroxy∆4tibolone, (5) 6-β-9-α-dihydroxy∆4tibolone (6) 7-β-hydroxy∆4tibolone) ] from fungi Cunninghemella Blakesleana to predict their possible biological targets and profiling of ADME properties.

METHOD

The prediction of pharmacokinetics properties membrane permeability as well as bioavailability radar properties were carried out by using Swiss target prediction, and Swiss ADME tools, respectively these metabolites were also subjected to predict the possible mechanism of action along with associated biological network pathways by using Reactome data-base.

RESULTS

All the six screened compounds possess excellent drug ability criteria, and exhibited exceptionally excellent non inhibitory potential against all five isozymes of CYP450 enzyme complex, including (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) respectively. All the screened compounds are lying within the acceptable pink zone of bioavailability radar and showing excellent descriptive properties. Compounds [1-4 & 6] are showing high BBB (Blood Brain Barrier) permeation, while compound 5 is exhibiting high HIA (Human Intestinal Absorption) property of (Egan Egg).

CONCLUSION

In conclusion, the results of this study smartly reveals that in-silico based studies are considered to provide robustness towards a rational drug designing and development approach, therefore in this way it helps to avoid the possibility of failure of drug candidates in the later experimental stages of drug development phases.

Longitudinal association between polypharmacy and development of pruritus: a Nationwide Cohort Study in a Japanese Population

BACKGROUND

Although polypharmacy is known to cause side-effects due to drug-drug interactions, dermatological symptoms triggered by polypharmacy are not fully addressed.

OBJECTIVE

To investigate whether polypharmacy is associated with the risk of pruritus.

METHOD

A cohort study was performed to examine cross-sectional and longitudinal relationships between polypharmacy and pruritus in a general population. Data were collected from the Norm Study conducted in 2016 and 2017, which is a nationwide survey based on a self-administered questionnaire with Japanese representative participants aged 16-84 years. Presence of polypharmacy which was defined as concurrent use of ≥5 prescribed drugs. Primary outcomes were the presence of severe pruritus at baseline for the cross-sectional analysis and the development of severe pruritus after one year for the longitudinal analysis. Multivariable modified Poisson regression analyses were performed to estimate risk ratios (RRs) and 95% confidence intervals (95%CIs) with adjustment for potential confounders (age, gender, smoking habits, drinking habits, depressive symptoms, moderate activities based on IPAQ score and presence of 11 comorbid conditions including skin disease).

RESULTS

The study included 3126 participants (mean age, 48.7 years); nearly half (49.8%) were male. In all, 332 participants (10.3%) had polypharmacy in the cross-sectional analysis. Participants with polypharmacy were more likely to present with severe pruritus at baseline than those who were not using drugs (adjusted RR = 1.52 [95%CI 1.15-2.01, P = 0.003]). The longitudinal analysis (n = 1803) was limited to those without severe pruritus at baseline; participants with polypharmacy at baseline were more likely to develop severe pruritus after a one-year follow-up period than those not using drugs (adjusted RR = 1.46 [95%CI 1.14-1.87, P = 0.002]).

CONCLUSION

Polypharmacy was associated with the presence of pruritus at baseline and may predict the future risk of developing pruritus.

Evaluation of Categorical Matrix Completion Algorithms: Towards Improved Active Learning for Drug Discovery

MOTIVATION

High throughput and high content screening are extensively used to determine the effect of small molecule compounds and other potential therapeutics upon particular targets as part of the early drug development process. However, screening is typically used to find compounds that have a desired effect but not to identify potential undesirable side effects. This is because the size of the search space precludes measuring the potential effect of all compounds on all targets. Active machine learning has been proposed as a solution to this problem.

RESULTS

In this article, we describe an improved imputation method, Impute By Committee, for completion of matrices containing categorical values. We compare this method to existing approaches in the context of modeling the effects of many compounds on many targets using latent similarities between compounds and conditions. We also compare these methods for the task of driving active learning in well-characterized settings for synthetic and real datasets. Our new approach performed the best overall both in the accuracy of matrix completion itself and in the number of experiments needed to train an accurate predictive model compared to random selection of experiments. We further improved upon the performance of our new method by developing an adaptive switching strategy for active learning that iteratively chooses between different matrix completion methods.

AVAILABILITY

A Reproducible Research Archive containing all data and code will be made available upon acceptance at http://murphylab.cbd.cmu.edu/software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Introducing the metacore concept for multi-target ligand design

In this work, we introduce the concept of "metacores" (MCs) for the organization of analog series (ASs) and multi-target (MT) ligand design. Generating compounds that are active against distantly related or unrelated targets is a central task in polypharmacology-oriented drug discovery. MCs are obtained by two-stage extraction of structural cores from ASs. The methodology is chemically intuitive and generally applicable. Each MC represents a set of related ASs and a template for the generation of new structures. We have systematically identified ASs that exclusively consisted of analogs with MT activity and determined their target profiles. From these ASs, a large set of 317 structurally diverse MCs was extracted, 127 of which were associated with different target families. The newly generated MCs were characterized and further prioritized on the basis of AS, compound, and target coverage. The analysis indicated that 260 MCs were pharmaceutically relevant. These MCs and the compound and target information they capture are made freely available for medicinal chemistry applications.

Siamese Recurrent Neural Network with a Self-Attention Mechanism for Bioactivity Prediction

Activity prediction plays an essential role in drug discovery by directing search of drug candidates in the relevant chemical space. Despite being applied successfully to image recognition and semantic similarity, the Siamese neural network has rarely been explored in drug discovery where modelling faces challenges such as insufficient data and class imbalance. Here, we present a Siamese recurrent neural network model (SiameseCHEM) based on bidirectional long short-term memory architecture with a self-attention mechanism, which can automatically learn discriminative features from the SMILES representations of small molecules. Subsequently, it is used to categorize bioactivity of small molecules via N-shot learning. Trained on random SMILES strings, it proves robust across five different datasets for the task of binary or categorical classification of bioactivity. Benchmarking against two baseline machine learning models which use the chemistry-rich ECFP fingerprints as the input, the deep learning model outperforms on three datasets and achieves comparable performance on the other two. The failure of both baseline methods on SMILES strings highlights that the deep learning model may learn task-specific chemistry features encoded in SMILES strings.

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network

Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals.

Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein–small molecule interactions, when sufficient drug–target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Does Exercise Influence Kynurenine/Tryptophan Metabolism and Psychological Outcomes in Persons With Age-Related Diseases? A Systematic Review

Background:

The kynurenine (KYN) pathway has been implicated in many diseases associated with inflammation and aging (“inflammaging”). Targeting the kynurenine pathway to modify disease outcomes has been trialled pharmacologically, but the evidence of non-pharmacological means (ie, exercise) remains unclear.

Objective:

We aim to assess the evidence of the effects of exercise on the kynurenine pathway and psychological outcomes.

Methods:

Under Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, a systematic literature search was performed in MEDLINE, EMBASE, EMCARE, and the Cochrane Central Registry of Controlled Trials. The main outcomes were changes in kynurenine pathway metabolite levels and psychological outcomes.

Results:

Six studies were analyzed (total n = 379) with exercise demonstrating significant concomitant effects on kynurenine pathway metabolite levels and associated psychological outcomes in domains of somatization, anxiety, and depression.

Conclusion:

Exercise has significant concomitant effect on kynurenine pathway metabolite levels and psychological outcomes. However, clear limitations exist in determining if the changes in the kynurenine pathway can fully explain the changes in psychological outcomes, or whether different diseases and exercise interventions act as confounding factors.

Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

MOTIVATION

In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases.

RESULTS

Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction of adverse drug reactions based on knowledge graph embedding

BACKGROUND

Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data.

METHOD

Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs.

RESULT

First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863.

CONCLUSION

In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.

Novel and versatile artificial intelligence algorithms for investigating possible GHSR1α and DRD1 agonists for Alzheimer's disease

Hippocampal lesions are recognized as the earliest pathological changes in Alzheimer's disease (AD). Recent researches have shown that the co-activation of growth hormone secretagogue receptor 1α (GHSR1α) and dopamine receptor D1 (DRD1) could recover the function of hippocampal synaptic and cognition. We combined traditional virtual screening technology with artificial intelligence models to screen multi-target agonists for target proteins from TCM database and a novel boost Generalized Regression Neural Network (GRNN) model was proposed in this article to improve the poor adjustability of GRNN. R-square was chosen to evaluate the accuracy of these artificial intelligent models. For the GHSR1α agonist dataset, Adaptive Boosting (AdaBoost), Linear Ridge Regression (LRR), Support Vector Machine (SVM), and boost GRNN achieved good results; the R-square of the test set of these models reached 0.900, 0.813, 0.708, and 0.802, respectively. For the DRD1 agonist dataset, Gradient Boosting (GB), Random Forest (RF), SVM, and boost GRNN achieved good results; the R-square of the test set of these models reached 0.839, 0.781, 0.763, and 0.815, respectively. According to these values of R-square, it is obvious that boost GRNN and SVM have better adaptability for different data sets and boost GRNN is more accurate than SVM. To evaluate the reliability of screening results, molecular dynamics (MD) simulation experiments were performed to make sure that candidates were docked well in the protein binding site. By analyzing the results of these artificial intelligent models and MD experiments, we suggest that 2007_17103 and 2007_13380 are the possible dual-target drugs for Alzheimer's disease (AD).

Drug design of new 5-HT6R antagonists aided by artificial neural networks

Alzheimer's Disease (AD) is the most frequent illness and cause of death amongst the age related-neurodegenerative disorders. The Alzheimer's Disease International (ADI) reported in 2019 that over 50 million people were living with dementia in the world and this number could potentially be around 152 million by 2050.5-hydroxtryptamine subtype 6 receptor (5-HT₆R) has been identified as a potential anti-amnesic drug target and therefore, the administration of 5-HT₆R antagonists can likely mitigate the memory loss and intellectual deterioration associated with AD. Herein, computational tools were applied to design new 5-HT₆ antagonists and their biological activity values were predicted by our QSAR model obtained from Artificial Neural Networks (ANN). The proposed compounds here from the QSAR-ANN model presented significant biological activity values and some of them have achieved pK_i above 9.00. Furthermore, our results suggest that the presence of halogen atoms (especially bromine) linked to the aromatic ring at para-position (HYD) contribute considerably to the increase of the biological activity values while bulky groups in the PI position do not culminate with the increase antagonist activity of compounds here analyzed. Finally, the ADME/Tox profile as well as the synthetic accessibility of new proposed compounds qualify them to go on further with experimental procedures and thenceforward their antagonist effects can be confirmed.

Finding Causal Mechanistic Drug-Drug Interactions from Observational Data

Many adverse drug reactions (ADRs) are caused by drug-drug interactions (DDIs), meaning they arise from concurrent use of multiple medications. Detecting DDIs using observational data has at least three major challenges: (1) The number of potential DDIs is astronomical; (2) Associations between drugs and ADRs may not be causal due to observed or unobserved confounding; and (3) Frequently co-prescribed drug pairs that each independently cause an ADR do not necessarily causally interact, where causal interaction means that at least some patients would only experience the ADR if they take both drugs. We address (1) through data mining algorithms pre-filtering potential interactions, and (2) and (3) by fitting causal interaction models adjusting for observed confounders and conducting sensitivity analyses for unobserved confounding. We rank candidate DDIs robust to unobserved confounding more likely to be real. Our rigorous approach produces far fewer false positives than past applications that ignored (2) and (3).

De Novo Prediction of Drug-Target Interactions Using Laplacian Regularized Schatten -Norm Minimization

In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug-target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.