Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Safranal alleviates pentetrazole-induced epileptic seizures in mice by inhibiting the NF-κB signaling pathway and mitochondrial-dependent apoptosis through GSK-3β inactivation

ETHNOPHARMACOLOGICAL RELEVANCE

Saffron, a traditional Chinese medicine, is derived from Crocus sativus L. stigmas and has been reported to possess neuroprotective properties and potentially contribute to the inhibition of apoptosis and inflammation. Safranal, a potent monothyral aldehyde, is a main component of saffron that has been reported to have antiepileptic activity. However, the specific mechanism by which safranal suppresses epileptic seizures via its antiapoptotic and anti-inflammatory properties is unclear.

AIM

To evaluate the effect of safranal on seizure severity, inflammation, and postictal neuronal apoptosis in a mouse model of pentetrazole (PTZ)-induced seizures and explore the underlying mechanism involved.

MATERIALS AND METHODS

The seizure stage and latency of stage 2 and 4 were quantified to assess the efficacy of safranal in mitigating PTZ-induced epileptic seizures in mice. Electroencephalography (EEG) was employed to monitor epileptiform afterdischarges in each experimental group. The cognitive abilities and motor functions of the mice were evaluated using the novel object recognition test and the open field test, respectively. Neurons were quantified using hematoxylin and eosin staining. Additionally, bioinformatics tools were utilized to predict the interactions between safranal and specific target proteins. Glycogen synthase kinase-3β (GSK-3β), mitochondrial apoptosis-related proteins, and inflammatory factor levels were analyzed through western blotting. Tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) concentrations in brain tissue were assessed by ELISA.

RESULTS

Safranal decreased the average seizure stage and increased the lantency of stage 2 and 4 seizures in PTZ-induced epileptic mice. Additionally, safranal exhibited neuroprotective effects on hippocampal CA1 and CA3 neurons and reduced hyperactivity caused by postictal hyperexcitability. Bioinformatics analysis revealed that safranal can bind to five specific proteins, including GSK-3β. By promoting Ser9 phosphorylation and inhibiting GSK-3β activity, safranal effectively suppressed the NF-κB signaling pathway. Moreover, the findings indicate that safranal treatment can decrease TNF-α and IL-1β levels in the cerebral tissues of epileptic mice and downregulate mitochondrial apoptosis-related proteins, including Bcl-2, Bax, Bak, Caspase 9, and Caspase 3.

CONCLUSION

Safranal can suppress the NF-κB signaling pathway and mitochondrial-dependent apoptosis through GSK-3β inactivation, suggesting that it is a promising therapeutic agent for epilepsy treatment.

Ophiopogonin D alleviates acute lung injury by regulating inflammation via the STAT3/A20/ASK1 axis

BACKGROUND

Acute lung injury (ALI) is characterized by acute pulmonary inflammatory infiltration. Alveolar epithelial cells (AECs) release numerous pro-inflammatory cytokines, which result in the pathological changes seen in ALI. Ophiopogonin D (OD), extracted from the roots of Ophiopogon japonicus (Thunb.) Ker Gawl. (Liliaceae), reduces inflammation; however, the efficacy of OD in ALI has not been reported and the underlying molecular mechanisms remain unclear.

PURPOSE

This study investigated the anti-inflammatory effects of OD, as well as the underlying mechanisms, in AECs and a mouse ALI model.

METHODS

Lipopolysaccharide (LPS) and tumor necrosis factor-α (TNF-α) were used to stimulate macrophages and A549 cells, and a mouse ALI model was established by intratracheal LPS administration. The anti-inflammatory effects and mechanisms of OD in the TNF-α-induced in vitro inflammation model was evaluated using real-time quantitative polymerase chain reaction qPCR), enzyme-linked immunosorbent assay (ELISA), western blotting, nuclear and cytoplasmic protein extraction, and immunofluorescence. The in vivo anti-inflammatory activity of OD was evaluated using hematoxylin and eosin staining, qPCR, ELISA, and western blotting.

RESULTS

The bronchoalveolar lavage fluid and lung tissue of LPS-induced ALI mice exhibited increased TNF-α expression. TNF-α induced a significantly greater pro-inflammatory effect in AECs than LPS. OD reduced inflammation and mitogen-activated protein kinase (MAPK) and transcription factor p65 phosphorylation in vivo and in vitro and promoted signal transducer and activator of transcription 3 (STAT3) phosphorylation and A20 expression, thereby inducing apoptosis signal-regulating kinase 1 (ASK1) proteasomal degradation.

CONCLUSION

OD exerts an anti-inflammatory effect by promoting STAT3-dependent A20 expression and ASK1 degradation. OD may therefore have therapeutic value in treating ALI and other TNF-α-related inflammatory diseases.

CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis

It is well-accepted that knowledge of a small molecule's target can accelerate optimization. Although chemogenomic databases are helpful resources for predicting or finding compound interaction partners, they tend to be limited and poorly annotated. Furthermore, unlike genes, compound identifiers are often not standardized, and many synonyms may exist, especially in the biological literature, making batch analysis of compounds difficult. Here, we constructed an open-source annotation and target hypothesis prediction tool that explores some of the largest chemical and biological databases, mining these for both common name, synonyms, and structurally similar molecules. We used this Chemical Analysis and Clustering for Target Identification (CACTI) tool to analyze the Pathogen Box collection, an open-source set of 400 drug-like compounds active against a variety of microbial pathogens. Our analysis resulted in 4,315 new synonyms, 35,963 pieces of new information and target prediction hints for 58 members.Scientific contributionsWith the employment of this tool, a comprehensive report with known evidence, close analogs and drug-target prediction can be obtained for large-scale chemical libraries that will facilitate their evaluation and future target validation and optimization efforts.

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a "few-shot learning" challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets. The code for FS-CAP is available at https://github.com/Rose-STL-Lab/FS-CAP.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.

Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite

The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson's disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.

A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

High Target Homology Does Not Guarantee Inhibition: Aminothiazoles Emerge as Inhibitors of Plasmodium falciparum

In this study, we identified three novel compound classes with potent activity against Plasmodium falciparum, the most dangerous human malarial parasite. Resistance of this pathogen to known drugs is increasing, and compounds with different modes of action are urgently needed. One promising drug target is the enzyme 1-deoxy-d-xylulose-5-phosphate synthase (DXPS) of the methylerythritol 4-phosphate (MEP) pathway for which we have previously identified three active compound classes against Mycobacterium tuberculosis. The close structural similarities of the active sites of the DXPS enzymes of P. falciparum and M. tuberculosis prompted investigation of their antiparasitic action, all classes display good cell-based activity. Through structure-activity relationship studies, we increased their antimalarial potency and two classes also show good metabolic stability and low toxicity against human liver cells. The most active compound 1 inhibits the growth of blood-stage P. falciparum with an IC50 of 600 nM. The results from three different methods for target validation of compound 1 suggest no engagement of DXPS. All inhibitor classes are active against chloroquine-resistant strains, confirming a new mode of action that has to be further investigated.

Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy

For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.

Alchemical analysis of FDA approved drugs

Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools designed for reaction informatics also work for alchemical processes that do not obey Lavoisier's principle, such as the transmutation of lead into gold. We start by using the differential reaction fingerprint (DRFP) to create tree-maps (TMAPs) representing the chemical space of pairs of drugs selected as being similar according to various molecular fingerprints. We then use the Transformer-based RXNMapper model to understand structural relationships between drugs, and its confidence score to distinguish between pairs related by chemically feasible transformations and pairs related by alchemical transmutations. This analysis reveals a diversity of structural similarity relationships that are otherwise difficult to analyze simultaneously. We exemplify this approach by visualizing FDA-approved drugs, EGFR inhibitors, and polymyxin B analogs.

Evaluation of the Antitumor Potential of Soloxolone Tryptamide against Glioblastoma Multiforme Using in silico, in vitro, and in vivo Approaches

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor characterized by uncontrollable diffusive growth, resistance to chemo- and radiotherapy, and a high recurrence rate leading to a low survival rate of patients with GBM. Due to a large number of signaling pathways regulating GBM pathogenesis, one of the promising directions is development of novel anti-glioblastoma compounds based on natural metabolites capable of affecting multiple targets. Here, we investigated the antitumor potential of the semisynthetic triterpenoid soloxolone tryptamide (STA) against human glioblastoma U87 cells. STA efficiently blocked the growth of U87 cells in 2D and 3D cultures, enhanced adhesiveness of tumor cells, and displayed synergistic cytotoxicity with temozolomide. In silico analysis suggested that the anti-glioblastoma activity of STA can be explained by its direct interaction with EGFR, ERBB2, and AKT1 which play an important role in the regulation of GBM malignancy. Along with direct effect on U87 cells, STA normalized tumor microenvironment in murine heterotopic U87 xenograft model by suppressing the development of immature blood vessels and elastin production in the tumor tissue. Taken together, our results clearly demonstrate that STA can be a novel promising antitumor candidate for GMB treatment.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Explainability and white box in drug discovery

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

Molecular Modeling and In Vitro Evaluation of Piplartine Analogs against Oral Squamous Cell Carcinoma

Cancer is a principal cause of death in the world, and providing a better quality of life and reducing mortality through effective pharmacological treatment remains a challenge. Among malignant tumor types, squamous cell carcinoma-esophageal cancer (EC) is usually located in the mouth, with approximately 90% located mainly on the tongue and floor of the mouth. Piplartine is an alkamide found in certain species of the genus Piper and presents many pharmacological properties including antitumor activity. In the present study, the cytotoxic potential of a collection of piplartine analogs against human oral SCC9 carcinoma cells was evaluated. The analogs were prepared via Fischer esterification reactions, alkyl and aryl halide esterification, and a coupling reaction with PyBOP using the natural compound 3,4,5-trimethoxybenzoic acid as a starting material. The products were structurally characterized using ¹H and ¹³C nuclear magnetic resonance, infrared spectroscopy, and high-resolution mass spectrometry for the unpublished compounds. The compound 4-methoxy-benzyl 3,4,5-trimethoxybenzoate (9) presented an IC₅₀ of 46.21 µM, high selectively (SI > 16), and caused apoptosis in SCC9 cancer cells. The molecular modeling study suggested a multi-target mechanism of action for the antitumor activity of compound 9 with CRM1 as the main target receptor.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine

Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important roles. However, most of the existing TCM ingredient databases do not provide simplification function for extracting key ingredients in each herb or formula, which hinders the research on the mechanism of actions of the ingredients in TCM databases. The lack of quality control and standardization of the data in most of these existing databases is also a prominent disadvantage. Therefore, we developed a Traditional Chinese Medicine Simplified Integrated Database (TCMSID) with high storage, high quality and standardization. The database includes 499 herbs registered in the Chinese pharmacopeia with 20,015 ingredients, 3270 targets as well as corresponding detailed information. TCMSID is not only a database of herbal ingredients, but also a TCM simplification platform. Key ingredients from TCM herbs are available to be screened out and regarded as representatives to explore the mechanism of TCM herbs by implementing multi-tool target prediction and multilevel network construction. TCMSID provides abundant data sources and analysis platforms for TCM simplification and drug discovery, which is expected to promote modernization and internationalization of TCM and enhance its international status in the future. TCMSID is freely available at https://tcm.scbdd.com .

Computational screening for new neuroprotective ingredients against Alzheimer's disease from bilberry by cheminformatics approaches

Bioactive ingredients from natural products have always been an important resource for the discovery of drugs for Alzheimer's disease (AD). Senile plaques, which are formed with amyloid-beta (Aβ) peptides and excess metal ions, are found in AD brains and have been suggested to play an important role in AD pathogenesis. Here, we attempted to design an effective and smart screening method based on cheminformatics approaches to find new ingredients against AD from Vaccinium myrtillus (bilberry) and verified the bioactivity of expected ingredients through experiments. This method integrated advanced artificial intelligence models and target prediction methods to realize the stepwise analysis and filtering of all ingredients. Finally, we obtained the expected new compound malvidin-3-O-galactoside (Ma-3-gal-Cl). The in vitro experiments showed that Ma-3-gal-Cl could reduce the OH· generation and intracellular ROS from the Aβ/Cu²⁺/AA mixture and maintain the mitochondrial membrane potential of SH-SY5Y cells. Molecular docking and Western blot results indicated that Ma-3-gal-Cl could reduce the amount of activated caspase-3 via binding with unactivated caspase-3 and reduce the expression of phosphorylated p38 via binding with mitogen-activated protein kinase kinases-6 (MKK6). Moreover, Ma-3-gal-Cl could inhibit the Aβ aggregation via binding with Aβ monomer and fibers. Thus, Ma-3-gal-Cl showed significant effects on protecting SH-SY5Y cells from Aβ/Cu²⁺/AA induced damage via antioxidation effect and inhibition effect to the Aβ aggregation.

Inhibition of the dopamine transporter promotes lysosome biogenesis and ameliorates Alzheimer's disease-like symptoms in mice

INTRODUCTION

Lysosomes are degradative organelles that maintain cellular homeostasis and protein quality control. Transcription factor EB (TFEB)-mediated lysosome biogenesis enhances lysosome-dependent degradation and alleviates neurodegenerative diseases, but the mechanisms underlying TFEB regulation and modification are still poorly understood.

METHODS

By screening novel small-molecule compounds, we identified a group of lysosome-enhancing compounds (LYECs) that promote TFEB activation and lysosome biogenesis.

RESULTS

One of these compounds, LH2-051, significantly inhibited the function of the dopamine transporter (DAT) and subsequently promoted lysosome biogenesis. We uncovered cyclin-dependent kinase 9 (CDK9) as a novel regulator of DAT-mediated lysosome biogenesis and identified six novel CDK9-phosphorylated sites on TFEB. We observed that signal transduction by the DAT-CDK9-TFEB axis occurs on lysosomes. Finally, we found that LH2-051 enhanced the degradation of amyloid beta plaques and improved the memory of amyloid precursor protein (APP)/Presenilin 1 (PS1) mice.

DISCUSSION

We identified the DAT-CDK9-TFEB signaling axis as a novel regulator of lysosome biogenesis. Our study sheds light on the mechanisms of protein quality control under pathophysiological conditions.

A network pharmacology-based approach to explore the therapeutic potential of Sceletium tortuosum in the treatment of neurodegenerative disorders

Sceletium tortuosum (SCT) has been utilized medicinally by indigenous Koi-San people purportedly for mood elevation. SCT extracts are reported to be neuroprotective and have efficacy in improving cognition. However, it is still unclear which of the pharmacological mechanisms of SCT contribute to the therapeutic potential for neurodegenerative disorders. Hence, this study investigated two aspects–firstly, the abilities of neuroprotective sub-fractions from SCT on scavenging radicals, inhibiting some usual targets relevant to Alzheimer’s disease (AD) or Parkinson’s disease (PD), and secondly utilizing the network pharmacology related methods to search probable mechanisms using Surflex-Dock program to show the key targets and corresponding SCT constituents. The results indicated sub-fractions from SCT could scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, inhibit acetylcholinesterase (AChE), monoamine oxidase type B (MAO-B) and N-methyl-D-aspartic acid receptor (NMDAR). Furthermore, the results of gene ontology and docking analyses indicated the key targets involved in the probable treatment of AD or PD might be AChE, MAO-B, NMDAR subunit2B (GluN2B-NMDAR), adenosine A_2A receptor and cannabinoid receptor 2, and the corresponding constituents in Sceletium tortuosum might be N-trans-feruloyl-3-methyldopamine, dihydrojoubertiamine and other mesembrine type alkaloids. In summary, this study has provided new evidence for the therapeutic potential of SCT in the treatment of AD or PD, as well as the key targets and notable constituents in SCT. Therefore, we propose SCT could be a natural chemical resource for lead compounds in the treatment of neurodegenerative disorders.

Activation of cAMP Signaling in Response to α-Phellandrene Promotes Vascular Endothelial Growth Factor Levels and Proliferation in Human Dermal Papilla Cells

Dermal papilla cells (DPCs) are growth factor reservoirs that are specialized for hair morphogenesis and regeneration. Due to their essential role in hair growth, DPCs are commonly used as an in vitro model to investigate the effects of hair growth-regulating compounds and their molecular mechanisms of action. Cyclic adenosine monophosphate (cAMP), an intracellular second messenger, is currently employed as a growth-promoting target molecule. In a pilot test, we found that α-phellandrene, a naturally occurring phytochemical, increased cAMP levels in DPCs. Therefore, we sought to determine whether α-phellandrene increases growth factors and proliferation in human DPCs and to identify the underlying mechanisms. We demonstrated that α-phellandrene promotes cell proliferation concentration-dependently. In addition, it increases the cAMP downstream effectors, such as protein kinase A catalytic subunit (PKA Cα) and phosphorylated cAMP-responsive element-binding protein (CREB). Also, among the CREB-dependent growth factor candidates, we identified that α-phellandrene selectively upregulated vascular endothelial growth factor (VEGF) mRNA expression in DPCs. Notably, the beneficial effects of α-phellandrene were nullified by a cAMP inhibitor. This study demonstrated the cAMP-mediated growth effects in DPCs and the therapeutic potential of α-phellandrene for preventing hair loss.

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.

Benzothiazole Derivatives Endowed with Antiproliferative Activity in Paraganglioma and Pancreatic Cancer Cells: Structure–Activity Relationship Studies and Target Prediction Analysis

The antiproliferative effects played by benzothiazoles in different cancers have aroused the interest for these molecules as promising antitumor agents. In this work, a library of phenylacetamide derivatives containing the benzothiazole nucleus was synthesized and compounds were tested for their antiproliferative activity in paraganglioma and pancreatic cancer cell lines. The novel synthesized compounds induced a marked viability reduction at low micromolar concentrations both in paraganglioma and pancreatic cancer cells. Derivative 4l showed a greater antiproliferative effect and higher selectivity index against cancer cells, as compared to other compounds. Notably, combinations of derivative 4l with gemcitabine at low concentrations induced enhanced and synergistic effects on pancreatic cancer cell viability, thus supporting the relevance of compound 4l in the perspective of clinical translation. A target prediction analysis was also carried out on 4l by using multiple computational tools, identifying cannabinoid receptors and sentrin-specific proteases as putative targets contributing to the observed antiproliferative activity.

Discovery of Novel Small Molecule HDAC1, 2, 3 Inhibitors -- Combined Receptor-Based and Ligand-Based Virtual Screening Strategy

Aims:

This study aims to investigate and validate the potential drug target to HDAC1.

Background:

Human histone deacetylase 1 (HDAC1) can catalyze the deacetylation of histones belonging to the family of human histone deacetylases (HDACs). Amide hydrolase HDAC1 plays a key role in the development of many serious cancers such as prostate cancer, gastric cancer, lung cancer, esophageal cancer, colon cancer, and breast cancer. Therefore, HDAC1 inhibitors, promoting the transcription of a series of key genes such as the p53 gene and inhibiting the development of cancer through various downstream mechanisms, have great potential for the treatment of cancer.

Objective:

The objective of this study is to discover new skeleton HDAC1 inhibitors efficiently and conveniently with therapeutic potential for cancer.

Method:

Based on the crystal structure of HDAC1, through the combination of receptor-based and ligand- based virtual screening from the commercial compound library, the top-ranked compounds are selected for purchase through binding modes analysis, and their activities were verified through in vitro HDAC1 inhibitory biological experiments.

Results:

Based on LeDock, 5ICN showed good distinguishing ability and was used as the receptor. According to the results of the LeDock docking scoring from receptor-based virtual screening, 69 compounds with binding energy less than -7.5 kcal/mol were obtained and used for ligand-based virtual screening. A total of 21 novel compounds with high potential HDAC1 inhibitory activity were collected by combining the similarity searching (NN) and the multinomial Naive Bayes machine learning model (NB) methods. Through binding modes analysis, 10 compounds with different structures with potential HDAC1 inhibitory activity were selected and screened HDAC1 inhibitory in vitro. May267 showed moderate HDAC1 inhibitory activity, and the inhibition rate was 48% at a concentration of 20 μM.

Conclusion:

This study discovers novel small molecule HDAC1 inhibitors by combined receptor-based and ligand-based virtual screening strategy, which provides an efficient method for the discovery of other small molecule drugs. May267 shows moderate HDAC1 inhibitory activity, which can be further optimized as a lead compound. However, it still has the problem of poor kinase selectivity to be solved.

Drugs-associated with red man syndrome: An integrative approach using disproportionality analysis and Pharmip

WHAT IS KNOWN AND OBJECTIVE

Red man syndrome (RMS) is a non-IgE-mediated anaphylactoid adverse event frequently witnessed after a rapid infusion of vancomycin. This study aims to unravel drugs and associated off-label targets that induce RMS by exploiting FDA Adverse Event Reporting System (FAERS) and Pharmacovigilance/Pharmacogenomics Insilico Pipeline (PHARMIP).

METHODS

The case/non-case retrospective observational study was conducted in the FAERS database. Reporting odds ratio (ROR) and proportional reporting ratio (PRR) data mining algorithms were used to evaluate the strength of the signal. The off-label targets of the drugs with potential signals were obtained using online servers by applying a similarity ensemble approach and a reverse pharmacophore database, which was further validated by molecular docking studies.

RESULTS AND DISCUSSION

Oritavancin exhibited a strong positive signal (PRR:1185.20 and ROR:1256), which suggests a higher risk for causing RMS. The literature search revealed the involvement of the MRGPRX2 gene in the development of RMS. PHARMIP study unearthed Carbonic anhydrase II (CA2) as the common off-label target among the drugs causing RMS. The results obtained from molecular docking studies reinforced the findings as mentioned earlier, wherein the highest docking score was disinterred for oritavancin (-9.4 for MRGPRX2 and - 8.7 for CA2).

WHAT IS NEW AND CONCLUSION

Many antibiotics and other classes of medications have been discovered in the quest for drugs that may induce RMS, although a causal relationship could not be established. The implication of MRGPX2 and CA2 in the initial stages of pathogenesis necessitates the development of inhibitors that could be used as potential therapeutic agents against RMS.

Identification of HMGCR as the anticancer target of physapubenolide against melanoma cells by in silico target prediction

Physapubenolide (PB), a withanolide-type compound extracted from the traditional herb Physalis minima L., has been demonstrated to exert remarkable cytotoxicity against cancer cells; however, its molecular mechanisms are still unclear. In this study, we demonstrated that PB inhibited cell proliferation and migration in melanoma cells by inducing cell apoptosis. The anticancer activity of PB was further verified in a melanoma xenograft model. To explore the mechanism underlying the anticancer effects of PB, we carried out an in silico target prediction study, which combined three approaches (chemical similarity searching, quantitative structure-activity relationship (QSAR), and molecular docking) to identify the targets of PB, and found that PB likely targets 3-hydroxy-methylglutaryl CoA reductase (HMGCR), the rate-limiting enzyme of the mevalonate pathway, which promotes cancer cell proliferation, migration, and metastasis. We further demonstrated that PB interacted with HMGCR, decreased its protein expression and inhibited the HMGCR/YAP pathway in melanoma cells. In addition, we found that PB could restore vemurafenib sensitivity in vemurafenib-resistant A-375 cells, which was correlated with the downregulation of HMGCR. In conclusion, we demonstrate that PB elicits anticancer action and enhances sensitivity to vemurafenib by targeting HMGCR.

PLATO: A Predictive Drug Discovery Web Platform for Efficient Target Fishing and Bioactivity Profiling of Small Molecules

PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Metabolomic profile and computational analysis for the identification of the potential anti-inflammatory mechanisms of action of the traditional medicinal plants Ocimum basilicum and Ocimum tenuiflorum

Ocimum basilicum and Ocimum tenuiflorum are two basil species widely used medicinally as an anti-inflammatory, antimicrobial and cardioprotective agent. This study focuses on the chemical characterization of the majoritarian compounds of both species and their anti-inflammatory potential. Up to 22 compounds such as various types of salvianolic acids, derivatives of rosmaniric acid and flavones were identified in both plants. The identified compounds were very similar between both plants and are consistent with previous finding in other studies in Portugal and Italy. Based on the identified molecules a consensus target prediction was carried out. Among the main predicted target proteins, we found a high representation of the carbonic anhydrase family (CA2, CA7 and CA12) and several key proteins from the arachidonic pathway (LOX5, PLA2, COX1 and COX2). Both pathways are well related to inflammation. The interaction between the compounds and these targets were explored through molecular docking and molecular dynamics simulation. Our results suggest that some molecules present in both plants can induce an anti-inflammatory response through a non-steroidal mechanism of action connected to the carbon dioxide metabolism.

Biological investigation of N-methyl thiosemicarbazones as antimicrobial agents and bacterial carbonic anhydrases inhibitors

The enormous burden of the COVID-19 pandemic in economic and healthcare terms has cast a shadow on the serious threat of antimicrobial resistance, increasing the inappropriate use of antibiotics and shifting the focus of drug discovery programmes from antibacterial and antifungal fields. Thus, there is a pressing need for new antimicrobials involving innovative modes of action (MoAs) to avoid cross-resistance rise. Thiosemicarbazones (TSCs) stand out due to their easy preparation and polypharmacological application, also in infectious diseases. Recently, we reported a small library of TSCs (1–9) that emerged for their non-cytotoxic behaviour. Inspired by their multifaceted activity, we investigated the antibacterial, antifungal, and antidermatophytal profiles of derivatives 1–9, highlighting a new promising research line. Furthermore, the ability of these compounds to inhibit selected microbial and human carbonic anhydrases (CAs) was assessed, revealing their possible involvement in the MoA and a good selectivity index for some derivatives.

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 Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure-Activity Relationship Model vs the Graph Convolutional Network

The prediction and evaluation of the biodegradability of molecules with computational methods are becoming increasingly important. Among the various methods, quantitative structure-activity relationship (QSAR) models have been demonstrated to predict the ready biodegradation of chemicals but have limited functionality owing to their complex implementation. In this study, we employ the graph convolutional network (GCN) method to overcome these issues. A biodegradability dataset from previous studies was trained to generate prediction models by (i) the QSAR models using the Mordred molecular descriptor calculator and MACCS molecular fingerprint and (ii) the GCN model using molecular graphs. The performance comparison of the methods confirms that the GCN model is more straightforward to implement and more stable; the specificity and sensitivity values are almost identical without specific descriptors or fingerprints. In addition, the performance of the models was further verified by randomly dividing the dataset into 100 different cases of training and test sets and by varying the test set ratio from 20 to 80%. The results of the current study clearly suggest the promise of the GCN model, which can be implemented straightforwardly and can replace conventional QSAR prediction models for various types and properties of molecules.

Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum

Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum. Some major challenges include the dearth of suitable animal models for anti-P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium. Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC₅₀ values of bioactive compounds against P. falciparum. The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.

A novel graph mining approach to predict and evaluate food-drug interactions

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.

Uncovering the anti-angiogenic effect of semisynthetic triterpenoid CDDO-Im on HUVECs by an integrated network pharmacology approach

AIM

To reveal the molecular mechanism of anti-angiogenic activity of semisynthetic triterpenoid CDDO-Im.

MATERIALS AND METHODS

Using re-analysis of cDNA microarray data of CDDO-Im-treated human vascular endothelial cells (HUVECs) (GSE71622), functional annotation of revealed differentially expressed genes (DEGs) and analysis of their co-expression, the key processes induced by CDDO-Im in HUVECs were identified. Venn diagram analysis was further performed to reveal the common DEGs, i.e. genes both susceptible to CDDO-Im and involved in the regulation of angiogenesis. A list of probable protein targets of CDDO-Im was prepared based on Connectivity Map/cheminformatics analysis and chemical proteomics data, among which the proteins that were most associated with the angiogenesis-related regulome were identified. Finally, identified targets were validated by molecular docking and text mining approaches.

KEY FINDINGS

The effect of CDDO-Im in HUVECs can be divided into two main phases: the short early phase (0.5-3 h) with an acute FOXD1/CEBPA/JUNB-regulated pro-angiogenic response induced by xenobiotic stress, and the second anti-angiogenic step (6-24 h) with massive suppression of various angiogenesis-related processes, accompanied by the activation of cytoprotective mechanisms. Our analysis showed that the anti-angiogenic activity of CDDO-Im is mediated by its inhibition of the expression of PLAT, ETS1, A2M, SPAG9, RASGRP3, FBXO32, GCNT1 and HDGFRP3 and its direct interactions with EGFR, mTOR, NOS2, HSP90AA1, MDM2, SYK, IRF3, ATR and KIF14.

SIGNIFICANCE

Our findings provide valuable insights into the understanding of the molecular mechanisms of the anti-angiogenic activity of cyano enone-bearing triterpenoids and revealed a range of novel promising therapeutic targets to control pathological neovascularization.

Inhibitors of Human Divalent Metal Transporters DMT1 (SLC11A2) and ZIP8 (SLC39A8) from a GDB-17 Fragment Library

Solute carrier proteins (SLCs) are membrane proteins controlling fluxes across biological membranes and represent an emerging class of drug targets. Here we searched for inhibitors of divalent metal transporters in a library of 1,676 commercially available 3D-shaped fragment-like molecules from the generated database GDB-17, which lists all possible organic molecules up to 17 atoms of C, N, O, S and halogen following simple criteria for chemical stability and synthetic feasibility. While screening against DMT1 (SLC11A2), an iron transporter associated with hemochromatosis and for which only very few inhibitors are known, only yielded two weak inhibitors, our approach led to the discovery of the first inhibitor of ZIP8 (SLC39A8), a zinc transporter associated with manganese homeostasis and osteoarthritis but with no previously reported pharmacology, demonstrating that this target is druggable.

Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach

We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC₅₀, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.

Computational healthcare: Present and future perspectives (Review)

Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.

Synthesis and biological evaluation of 3,5-diaryl-pyrazole derivatives as potential antiprostate cancer agents

Prostate cancer is the most frequently diagnosed tumor in men and the second leading cause of cancer-associated mortality in most developed countries. 3,5-Diaryl substituted pyrazole derivatives (20-28) were prepared starting from related chalcones and biologically evaluated for in vitro growth inhibition activity against PC3 and DU145 human prostate cancer cell lines. Compounds 23, 26, and 28 were found to be more potent as compared to the other halogen-substituted derivatives. Especially, the 2-bromo-substituted pyrazole derivative (26) was found to be more potent against PC3 and DU145 cells. Epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor 2 (VEGFR2) are known to be expressed in DU145 and PC3 cancer cells. The binding mode of the most selective compound 26 toward EGFR and VEGFR2 was investigated by employing docking simulations based on GLIDE standard precision (-5.912 and -6.949 kcal/mol, respectively).

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

Computational modeling predicts potential effects of the herbal infusion "horchata" against COVID-19

Bioactive plant-derived molecules have emerged as therapeutic alternatives in the fight against the COVID-19 pandemic. In this investigation, principal bioactive compounds of the herbal infusion “horchata” from Ecuador were studied as potential novel inhibitors of the SARS-CoV-2 virus. The chemical composition of horchata was determined through a HPLC-DAD/ESI-MSⁿ and GC–MS analysis while the inhibitory potential of the compounds on SARS-CoV-2 was determined by a computational prediction using various strategies, such as molecular docking and molecular dynamics simulations. Up to 51 different compounds were identified. The computational analysis of predicted targets reveals the compounds’ possible anti-inflammatory (no steroidal) and antioxidant effects. Three compounds were identified as candidates for M^pro inhibition: benzoic acid, 2-(ethylthio)-ethyl ester, l-Leucine-N-isobutoxycarbonyl-N-methyl-heptyl and isorhamnetin and for PL^pro: isorhamnetin-3-O-(6-Orhamnosyl-galactoside), dihydroxy-methoxyflavanone and dihydroxyphenyl)-5-hydroxy-4-oxochromen-7-yl]oxy-3,4,5-trihydroxyoxane-2-carboxylic acid. Our results suggest the potential of Ecuadorian horchata infusion as a starting scaffold for the development of new inhibitors of the SARS-CoV-2 M^pro and PL^pro enzymes.

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.

Epigenetic Target Fishing with Accurate Machine Learning Models

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.