PubChem BioAssay: 2014 update

Characterization of a unique catechol-O-methyltransferase as a molecular drug target in parasitic filarial nematodes

BACKGROUND

Filarial nematodes cause severe illnesses in humans and canines including limb deformities and disfigurement, heart failure, blindness, and death, among others. There are no vaccines, and current drugs against filarial nematodes infections have only modest effects and are prone to complications.

METHODOLOGY/PRINCIPAL FINDINGS

We identified a gene (herein called DiMT) encoding an S-adenosyl-L-methionine (SAM)-dependent methyltransferase with orthologs in parasite filarial worms but not in mammals. By in silico analysis, DiMT possesses catalytic sites for binding SAM and catecholamines with high affinity. We expressed and purified recombinant DiMT protein and used it as an enzyme in a series of SAM-dependent methylation assays. DiMT acted specifically as a catechol-O-methyltransferase (COMT), catalyzing catabolic methylation of dopamine, and depicted Michaelis Menten kinetics on substrate and co-substrate. Among a set of SAM-dependent methyltransferase inhibitors, we identified compounds that bound with high affinity to DiMT's catalytic sites and inhibited its enzymatic activity. By testing the efficacy of DiMT inhibitors against microfilariae of Dirofilaria immitis in culture, we identified three inhibitors with concentration- and time-dependent effect of killing D. immitis microfilariae. Importantly, RNAi silencing of a DiMT ortholog in Caenorhabditis elegans has been shown to be lethal, likely as a result of excessive accumulation of active catecholamines that inhibit worm locomotion, pharyngeal pumping and fecundity.

CONCLUSIONS/SIGNIFICANCE

Together, we have unveiled DiMT as an essential COMT that is conserved in parasitic filarial nematodes, but is significantly different from mammalian COMTs and, therefore, is a viable target for development of novel drugs against filarial nematode infections.

The mechanism of Gejie Zhilao Pill in treating tuberculosis based on network pharmacology and molecular docking verification

Introduction

Gejie Zhilao Pill (GJZLP), a traditional Chinese medicine formula is known for its unique therapeutic effects in treating pulmonary tuberculosis. The aim of this study is to further investigate its underlying mechanisms by utilizing network pharmacology and molecular docking techniques.

Methods

Using TCMSP database the components, potential targets of GJZLP were identified. Animal-derived components were supplemented through the TCMID and BATMAN-TCM databases. Tuberculosis-related targets were collected from the TTD, OMIM, and GeneCards databases. The intersection target was imported into the String database to build the PPI network. The Metascape platform was employed to carry out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Heatmaps were generated through an online platform (https://www.bioinformatics.com.cn). Molecular docking was conducted between the core targets and core compounds to explore their binding strengths and patterns at the molecular level.

Results

61 active ingredients and 118 therapeutic targets were identified. Quercetin, Luteolin, epigallocatechin gallate, and beta-sitosterol showed relatively high degrees in the network. IL6, TNF, JUN, TP53, IL1B, STAT3, AKT1, RELA, IFNG, and MAPK3 are important core targets. GO and KEGG revealed that the effects of GJZLP on tuberculosis mainly involve reactions to bacterial molecules, lipopolysaccharides, and cytokine stimulation. Key signaling pathways include TNF, IL-17, Toll-like receptor and C-type lectin receptor signaling. Molecular docking analysis demonstrated a robust binding affinity between the core compounds and the core proteins. Stigmasterol exhibited the lowest binding energy with AKT1, indicating the most stable binding interaction.

Discussion

This study has delved into the efficacious components and molecular mechanisms of GJZLP in treating tuberculosis, thereby highlighting its potential as a promising therapeutic candidate for the treatment of tuberculosis.

Antifungal drug discovery for targeting Candida albicans morphogenesis through structural dynamics study

In response to the escalating threat of drug-resistant fungi to human health, there is an urgent need for innovative strategies. Our focus is on addressing this challenge by exploring a previously untapped target, yeast casein kinase (Yck2), as a potential space for antifungal development. To identify promising antifungal candidates, we conducted a thorough screening of the diverse-lib drug-like molecule library, comprising 99,288 molecules. Five notable drug-like compounds with diverse-lib IDs 24334243, 24342416, 17516746, 17407455, and 24360740 were selected based on their binding energy scores surpassing 11 Kcal/mol. Our investigation delved into the interaction studies and dynamic stability of these compounds. Remarkably, all selected molecules demonstrated acceptable RMSD values during the 200 ns simulation, indicating their stable nature. Further analysis through Principal Component Analysis (PCA)-based Free Energy Landscape (FEL) revealed minimal energy transitions for most compounds, signifying dynamic stability. Notably, the two compounds exhibited slightly different behaviour in terms of energy transitions. These findings mark a significant breakthrough in the realm of antifungal drugs against C. albicans by targeting the Yck2 protein. However, it is crucial to note that additional experimental validation is imperative to assess the efficacy of these molecules as potential antifungal candidates. This study serves as a promising starting point for further exploration and development in the quest for effective antifungal solutions.Communicated by Ramaswamy H. Sarma.

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

Design, synthesis, in-vitro biological profiling and molecular docking of some novel oxazolones and imidazolones exhibiting good inhibitory potential against acetylcholine esterase

Heterocyclic compounds with oxazole and imidazole rings in their structure have disclosed momentous biological aptitudes. Taking into account their superlative attributes, the present study was designed to introduce a new synthetic scheme to make new derivatives with tremendous futuristic pharmacological potentialities. Series of Oxazolones were synthesized by using substituted benzaldehyde with benzyl halides to produce respective benzaldehyde derivatives 1 (a-d) which further reacted with hippuric acid to yield oxazolones 2 (a-e). Newly synthesized oxazolones then reacted with 4-chloroaniline to yield corresponding imidazolones 3 (a-e). All the compounds were characterized by using FTIR and NMR spectroscopic techniques. Docking studies of Compounds were conducted using AutoDock Vina and analyzed with PYMOL. All synthesized oxazolone and imidazolone derivatives exhibited antioxidant potential, demonstrated by their IC50 values compared to ascorbic acid standard. Oxazolone derivatives (2a-2e) exhibited good acetyl cholinesterase inhibitory potential whereas Imidazolone series did not show significant inhibition as shown by their IC50 values compared to donepezil as a standard. Docking studies of all compounds against acetylcholinesterase demonstrated favorable binding affinity, indicating their potential for further in-vivo studies. It is notable that novel compounds of both oxazolones and Imidazolone series exhibited antioxidant potential with maximum percentage inhibition of 75.9 (IC50 12.9 ± 0.0573 µM/mL) by compound 2d while compound 2a showed AChE inhibitory potential with maximum %age inhibition of 75.49 (IC50 7.8 ± 0.0218 µM/mL).Communicated by Ramaswamy H. Sarma.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

A Bio-Indicator Pilot Study Screening Selected Heavy Metals in Female Hair, Nails, and Serum from Lifestyle Cosmetic, Canned Food, and Manufactured Drink Choices

Lifestyles, genetic predispositions, environmental factors, and geographical regions are considered key factors of heavy metals initiatives related to health issues. Heavy metals enter the body via the environment, daily lifestyle, foods, beverages, cosmetics, and other products. The accumulation of heavy metals in the human body leads to neurological issues, carcinogenesis, failure of multiple organs in the body, and a reduction in sensitivity to treatment. We screened for Cr, Al, Pb, and Cd in selected foods, beverages, and cosmetics products depending on questionnaire outcomes from female volunteers. We also screened for Cr, Al, Pb, and Cd on hair, nails, and serum samples using inductively coupled plasma mass spectrometry (ICP-MS) from the same volunteers, and we analyzed the serum cholinesterase and complete blood picture (CBC). We performed an AutoDock study on Cr, Al, Pb, and Cd as potential ligands. Our results indicate that the most elevated heavy metal in the cosmetic sample was Al. In addition, in the food and beverages samples, it was Pb and Al, respectively. The results of the questionnaire showed that 71 percent of the female volunteers used the studied cosmetics, food, and beverages, which were contaminated with Cr, Al, Pb, and Cd, reflecting the high concentration of Cr, Al, Cd, and Pb in the three different types of biological samples of sera, nails, and hair of the same females, with 29 percent of the female volunteers not using the products in the studied samples. Our results also show an elevated level of cholinesterase in the serum of group 1 that was greater than group 2, and this result was confirmed by AutoDock. Moreover, the negative variation in the CBC result was compared with the reference ranges. Future studies should concentrate on the actions of these heavy metal contaminations and their potential health consequences for various human organs individually.

A comparison of approaches to accessing existing biological and chemical relational databases via SPARQL

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project.

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in image synthesis, Generative Adversarial Networks (GANs), and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.

Pharmacophore-Oriented Identification of Potential Leads as CCR5 Inhibitors to Block HIV Cellular Entry

Cysteine-cysteine chemokine receptor 5 (CCR5) has been discovered as a co-receptor for cellular entry of human immunodeficiency virus (HIV). Moreover, the role of CCR5 in a variety of cancers and various inflammatory responses was also discovered. Despite the fact that several CCR5 antagonists have been investigated in clinical trials, only Maraviroc has been licensed for use in the treatment of HIV patients. This indicates that there is a need for novel CCR5 antagonists. Keeping this in mind, the present study was designed. The active CCR5 inhibitors with known IC₅₀ value were selected from the literature and utilized to develop a ligand-based common feature pharmacophore model. The validated pharmacophore model was further used for virtual screening of drug-like databases obtained from the Asinex, Specs, InterBioScreen, and Eximed chemical libraries. Utilizing computational methods such as molecular docking studies, molecular dynamics simulations, and binding free energy calculation, the binding mechanism of selected inhibitors was established. The identified Hits not only showed better binding energy when compared to Maraviroc, but also formed stable interactions with the key residues and showed stable behavior throughout the 100 ns MD simulation. Our findings suggest that Hit1 and Hit2 may be potential candidates for CCR5 inhibition, and, therefore, can be considered for further CCR5 inhibition programs.

Phosphate position is key in mediating transmembrane ion channel TMEM16A-phosphatidylinositol 4,5-bisphosphate interaction

TransMEMbrane 16A (TMEM16A) is a Ca2+-activated Cl- channel that plays critical roles in regulating diverse physiologic processes, including vascular tone, sensory signal transduction, and mucosal secretion. In addition to Ca2+, TMEM16A activation requires the membrane lipid phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). However, the structural determinants mediating this interaction are not clear. Here, we interrogated the parts of the PI(4,5)P2 head group that mediate its interaction with TMEM16A by using patch- and two-electrode voltage-clamp recordings on oocytes from the African clawed frog Xenopus laevis, which endogenously express TMEM16A channels. During continuous application of Ca2+ to excised inside-out patches, we found that TMEM16A-conducted currents decayed shortly after patch excision. Following this rundown, we show that the application of a synthetic PI(4,5)P2 analog produced current recovery. Furthermore, inducible dephosphorylation of PI(4,5)P2 reduces TMEM16A-conducted currents. Application of PIP2 analogs with different phosphate orientations yielded distinct amounts of current recovery, and only lipids that include a phosphate at the 4' position effectively recovered TMEM16A currents. Taken together, these findings improve our understanding of how PI(4,5)P2 binds to and potentiates TMEM16A channels.

Protein-Ligand Docking in the Machine-Learning Era

Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

End-to-end learning for compound activity prediction based on binding pocket information

BACKGROUND

Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, docking simulation is generally applied because it only requires a tertiary structure of the target protein. However, the conformation search and the evaluation of binding energy of docking simulation are computationally heavy and thus docking simulation needs huge computational resources. Thus, if we can apply a machine learning-based activity prediction method for a novel target protein, such methods would be highly useful. Recently, Tsubaki et al. proposed an end-to-end learning method to predict the activity of compounds for novel target proteins. However, the prediction accuracy of the method was still insufficient because it only used amino acid sequence information of a protein as the input.

RESULTS

In this research, we proposed an end-to-end learning-based compound activity prediction using structure information of a binding pocket of a target protein. The proposed method learns the important features by end-to-end learning using a graph neural network both for a compound structure and a protein binding pocket structure. As a result of the evaluation experiments, the proposed method has shown higher accuracy than an existing method using amino acid sequence information.

CONCLUSIONS

The proposed method achieved equivalent accuracy to docking simulation using AutoDock Vina with much shorter computing time. This indicated that a machine learning-based approach would be promising even for novel target proteins in activity prediction.

Identification of COVID-19 and Dengue Host Factor Interaction Networks Based on Integrative Bioinformatics Analyses

Background

The outbreak of Coronavirus disease 2019 (COVID-19) has become an international public health crisis, and the number of cases with dengue co-infection has raised concerns. Unfortunately, treatment options are currently limited or even unavailable. Thus, the aim of our study was to explore the underlying mechanisms and identify potential therapeutic targets for co-infection.

Methods

To further understand the mechanisms underlying co-infection, we used a series of bioinformatics analyses to build host factor interaction networks and elucidate biological process and molecular function categories, pathway activity, tissue-specific enrichment, and potential therapeutic agents.

Results

We explored the pathologic mechanisms of COVID-19 and dengue co-infection, including predisposing genes, significant pathways, biological functions, and possible drugs for intervention. In total, 460 shared host factors were collected; among them, CCL4 and AhR targets were important. To further analyze biological functions, we created a protein-protein interaction (PPI) network and performed Molecular Complex Detection (MCODE) analysis. In addition, common signaling pathways were acquired, and the toll-like receptor and NOD-like receptor signaling pathways exerted a significant effect on the interaction. Upregulated genes were identified based on the activity score of dysregulated genes, such as IL-1, Hippo, and TNF-α. We also conducted tissue-specific enrichment analysis and found ICAM-1 and CCL2 to be highly expressed in the lung. Finally, candidate drugs were screened, including resveratrol, genistein, and dexamethasone.

Conclusions

This study probes host factor interaction networks for COVID-19 and dengue and provides potential drugs for clinical practice. Although the findings need to be verified, they contribute to the treatment of co-infection and the management of respiratory disease.

On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities

For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC₅₀ predictions can screen molecules that have desirable activity before assessment by docking studies.

Curated Data In - Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing

New Approach Methodologies (NAMs) that employ artificial intelligence (AI) for predicting adverse effects of chemicals have generated optimistic expectations as alternatives to animal testing. However, the major underappreciated challenge in developing robust and predictive AI models is the impact of the quality of the input data on the model accuracy. Indeed, poor data reproducibility and quality have been frequently cited as factors contributing to the crisis in biomedical research, as well as similar shortcomings in the fields of toxicology and chemistry. In this article, we review the most recent efforts to improve confidence in the robustness of toxicological data and investigate the impact that data curation has on the confidence in model predictions. We also present two case studies demonstrating the effect of data curation on the performance of AI models for predicting skin sensitisation and skin irritation. We show that, whereas models generated with uncurated data had a 7-24% higher correct classification rate (CCR), the perceived performance was, in fact, inflated owing to the high number of duplicates in the training set. We assert that data curation is a critical step in building computational models, to help ensure that reliable predictions of chemical toxicity are achieved through use of the models.

Recent Design and Structure-Activity Relationship Studies on the Modifications of DHFR Inhibitors as Anticancer Agents

BACKGROUND

Dihydrofolate reductase (DHFR) has been known for decades as a molecular target for antibacterial, antifungal and anti-malarial treatments. This enzyme is becoming increasingly important in the design of new anticancer drugs, which is confirmed by numerous studies including modelling, synthesis and in vitro biological research. This review aims to present and discuss some remarkable recent advances in the research of new DHFR inhibitors with potential anticancer activity.

METHODS

The scientific literature of the last decade on the different types of DHFR inhibitors has been searched. The studies on design, synthesis and investigation structure-activity relationships were summarized and divided into several subsections depending on the leading molecule and its structural modification. Various methods of synthesis, potential anticancer activity and possible practical applications as DHFR inhibitors of new chemical compounds were described and discussed.

RESULTS

This review presents the current state of knowledge on the modification of known DHFR inhibitors and the structures and searches for about eighty new molecules, designed as potential anticancer drugs. In addition, DHFR inhibitors acting on thymidylate synthase (TS), carbon anhydrase (CA) and even DNA-binding are presented in this paper.

CONCLUSION

Thorough physicochemical characterization and biological investigations highlight the structure-activity relationship of DHFR inhibitors. This will enable even better design and synthesis of active compounds, which would have the expected mechanism of action and the desired activity.

Exploration of databases and methods supporting drug repurposing: a comprehensive survey

Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.

Artificial intelligence in the early stages of drug discovery

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

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

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

Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022-0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP⁺⁺), and further improved the ethnicity-specific validations (0.759 ± 0.066-0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Suppression of Drug-Resistant Non-Small-Cell Lung Cancer with Inhibitors Targeting Minichromosomal Maintenance Protein

Drug resistance has been a major threat in cancer therapies that necessitates the development of new strategies to overcome this problem. We report here a cell-based high-throughput screen of a library containing two-million molecules for the compounds that inhibit the proliferation of non-small-cell lung cancer (NSCLC). Through the process of phenotypic screening, target deconvolution, and structure-activity relationship (SAR) analysis, a compound of furanonaphthoquinone-based small molecule, AS4583, was identified that exhibited potent activity in tyrosine kinase inhibitor (TKI)-sensitive and TKI-resistant NSCLC cells (IC₅₀ = 77 nM) and in xenograft mice. The mechanistic studies revealed that AS4583 inhibited cell-cycle progression and reduced DNA replication by disrupting the formation of the minichromosomal maintenance protein (MCM) complex. Subsequent SAR study of AS4583 gave compound RJ-LC-07-48 which exhibited greater potency in drug-resistant NSCLC cells (IC₅₀ = 17 nM) and in mice with H1975 xenograft tumor.

Diversity-Oriented Library Synthesis from Steviol and Isosteviol-Derived Scaffolds

The readily available natural product stevioside provides a unique diterpene core structure that can be explored for small molecule library development by diversity-oriented synthesis and functional group transformations. Validation arrays were prepared from steviol, isosteviol, and related analogues, derived from stevioside, to produce over 90 compounds. These compounds were submitted to the NIH Molecular Libraries Small Molecule Repository for screening in the Molecular Libraries Screening Center Network. Micromolar hits were identified in multiple high-throughput assays for several library members. A cheminformatics analysis of the compounds was performed that verified the expected diversity and complexity of this set of compounds. The screening results indicate that scaffolds-derived natural products can provide screening hits against multiple target proteins.

DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug–target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

Using Machine Learning to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List

A recent OECD report estimated that more than 4000 per- and polyfluorinated alkyl substances (PFASs) have been produced and used in a broad range of industrial and consumer applications. However, little is known about the potential hazards (e.g., bioactivity, bioaccumulation, and toxicity) of most PFASs. Here, we built machine-learning-based quantitative structure-activity relationship (QSAR) models to predict the bioactivity of those PFASs. By examining a number of available molecular data sets, we constructed the first PFAS-specific database that contains the bioactivity information on 1012 PFASs for 26 bioassays. On the basis of the collected PFAS data set, we trained 5 different machine learning models that cover a variety of conventional models (e.g., random forest and multitask neural network (MNN)) and advanced graph-based models (e.g., graph convolutional network). Those models were evaluated based on the validation data set. Both MNN and graph-based models demonstrated the best performance. The average of the best area-under-the-curve score for each bioassay is 0.916. For predictions on the OECD list, most of the biologically active PFASs have perfluoroalkyl chain lengths less than 12 and are categorized into fluorotelomer-related compounds and perfluoroalkyl acids and their precursors.

Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research

In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.

Isolation of Cardiomyocytes Undergoing Mitosis With Complete Cytokinesis

RATIONALE

Adult human cardiomyocytes do not complete cytokinesis despite passing through the S-phase of the cell cycle. As a result, polyploidization and multinucleation occur. To get a deeper understanding of the mechanisms surrounding division of cardiomyocytes, there is a crucial need for a technique to isolate cardiomyocytes that complete cell division/cytokinesis.

OBJECTIVE

Markers of cell cycle progression based on DNA content cannot distinguish between mitotic cardiomyocytes that fail to complete cytokinesis from those cells that undergo true cell division. With the use of molecular beacons (MBs) targeting specific mRNAs, we aimed to identify truly proliferative cardiomyocytes derived from human induced pluripotent stem cells.

METHODS AND RESULTS

Fluorescence-activated cell sorting combined with MBs was performed to sort cardiomyocyte populations enriched for mitotic cells. Expressions of cell cycle specific genes were confirmed by means of reverse transcription-quantitative polymerase chain reaction and single-cell RNA sequencing (scRNA-seq) combined with gene signatures of cell cycle progression. We characterized the sorted groups by proliferation assays and time-lapse microscopy which confirmed the proliferative advantage of MB-positive cell populations relative to MB-negative and G2/M populations. Gene expression analysis revealed that the MB-positive cardiomyocyte subpopulation exhibited patterns consistent with the processes of nuclear division, chromosome segregation, and transition from M to G1 phase. The use of dual-MBs targeting CDC20 and SPG20 mRNAs enabled the enrichment of cytokinetic events (CDC20^highSPG20^high). Interestingly, cells that did not complete cytokinesis and remained binucleated were found to be CDC20^lowSPG20^high while polyploid cardiomyocytes that replicated DNA but failed to complete karyokinesis were found to be CDC20^lowSPG20^low.

CONCLUSIONS

This study demonstrates a novel alternative to existing DNA content-based approaches for sorting cardiomyocytes with true mitotic potential that can be used to study the unique dynamics of cardiomyocyte nuclei during mitosis. Our technique for sorting live cardiomyocytes undergoing cytokinesis would provide a basis for future studies to uncover mechanisms underlying the development and regeneration of heart tissue.

EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge

Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and pharmaceutical companies because it reduces the cost and duration of the drug development pipeline and the likelihood of unforeseen adverse events. To date there has not been a systematic effort to identify such opportunities, in part because of the lack of a comprehensive resource for an enormous amount of unsystematic drug repositioning information to support scientists who could benefit from this endeavor. To address this challenge, we developed a new database, the Experimental Knowledge-Based Drug Repositioning Database (EK-DRD), by using text and data mining as well as manual curation. EK-DRD contains experimentally validated drug repositioning annotation for 1861 FDA-approved and 102 withdrawn small-molecule drugs. Annotation was done at four levels using 30 944 target assay records, 3999 cell assay records, 585 organism assay records, and 8889 clinical trial records. Additionally, approximately 1799 repositioning protein or target sequences coupled with 856 related diseases and 1332 pathways are linked to the drug entries. Our web-based software displays a network of integrative relationships between drugs, their repositioning targets, and related diseases. The database is fully searchable and supports extensive text, sequence, chemical structure, and relational query searches. EK-DRD is freely accessible at http://www.idruglab.com/drd/index.php .

Machine Learning for Molecular Modelling in Drug Design

Machine learning (ML) has become a crucial component of early drug discovery. This researcharea has been fueled by two main factors [...].

Drug repurposing for antimicrobial discovery

Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.

CSgator: an integrated web platform for compound set analysis

Drug discovery typically involves investigation of a set of compounds (e.g. drug screening hits) in terms of target, disease, and bioactivity. CSgator is a comprehensive analytic tool for set-wise interpretation of compounds. It has two unique analytic features of Compound Set Enrichment Analysis (CSEA) and Compound Cluster Analysis (CCA), which allows batch analysis of compound set in terms of (i) target, (ii) bioactivity, (iii) disease, and (iv) structure. CSEA and CCA present enriched profiles of targets and bioactivities in a compound set, which leads to novel insights on underlying drug mode-of-action, and potential targets. Notably, we propose a novel concept of 'Hit Enriched Assays", i.e. bioassays of which hits are enriched among a given set of compounds. As an example, we show its utility in revealing drug mode-of-action or identifying hidden targets for anti-lymphangiogenesis screening hits. CSgator is available at http://csgator.ewha.ac.kr , and most analytic results are downloadable.

Deep learning for predicting toxicity of chemicals: a mini review

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

Identifying de-NEDDylation inhibitors: Virtual high-throughput screens targeting SENP8

Protein modification can have far-reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high-throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over-NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high-throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high-throughput screening improved hit rates over traditional high-throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.

In Silico Study Probes Potential Inhibitors of Human Dihydrofolate Reductase for Cancer Therapeutics

Dihydrofolate reductase (DHFR) is an essential cellular enzyme and thereby catalyzes thereduction of dihydrofolate to tetrahydrofolate (THF). In cancer medication, inhibition of humanDHFR (hDHFR) remains a promising strategy, as it depletes THF and slows DNA synthesis and cellproliferation. In the current study, ligand-based pharmacophore modeling identified and evaluatedthe critical chemical features of hDHFR inhibitors. A pharmacophore model (Hypo1) was generatedfrom known inhibitors of DHFR with a correlation coefficient (0.94), root mean square (RMS)deviation (0.99), and total cost value (125.28). Hypo1 was comprised of four chemical features,including two hydrogen bond donors (HDB), one hydrogen bond acceptor (HBA), and onehydrophobic (HYP). Hypo1 was validated using Fischer's randomization, test set, and decoy setvalidations, employed as a 3D query in a virtual screening at Maybridge, Chembridge, Asinex,National Cancer Institute (NCI), and Zinc databases. Hypo1-retrieved compounds were filtered byan absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment test andLipinski's rule of five, where the drug-like hit compounds were identified. The hit compounds weredocked in the active site of hDHFR and compounds with Goldfitness score was greater than 44.67(docking score for the reference compound), clustering analysis, and hydrogen bond interactionswere identified. Furthermore, molecular dynamics (MD) simulation identified three compounds asthe best inhibitors of hDHFR with the lowest root mean square deviation (1.2 Å to 1.8 Å), hydrogenbond interactions with hDHFR, and low binding free energy (-127 kJ/mol to -178 kJ/mol). Finally,the toxicity prediction by computer (TOPKAT) affirmed the safety of the novel inhibitors of hDHFRin human body. Overall, we recommend novel hit compounds of hDHFR for cancer and rheumatoidarthritis chemotherapeutics.

Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation

Glycolysis with PK-M2 occurs typically in anaerobic conditions and atypically in aerobic conditions, which is known as the Warburg effect. The Warburg effect is found in many oncogenic situations and is believed to provide energy and biomass for oncogenesis to persist. The work presented targets human PK-M2 (hPK-M2) in a virtual high-throughput screen to identify new inhibitors and leads for further study. In the initial screen, one of the 12 candidates selected for experimental validation showed biological activity (hit-rate = 8.13%). In the second screen with retrained models, six of 11 candidates selected for experimental validation showed biological activity (hit-rate: 54.5%). Additionally, four different scaffolds were identified for further analysis when examining the tested candidates and compounds in the training data. Finally, extrapolation was necessary to identify a sufficient number of candidates to test in the second screen. Examination of the results suggested stepwise extrapolation to maximize efficiency.

In Silico Approaches to Predict Drug-Transporter Interaction Profiles: Data Mining, Model Generation, and Link to Cholestasis

Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as cholestasis, which comprises a major challenge in drug development process. Thus, computer-based (in silico) models that can predict the pharmacological and toxicological profiles of these small molecules with respect to liver transporters may help in the early prioritization of compounds and hence may lower the high attrition rates. In this chapter, we provide a protocol for in silico prediction of cholestasis by generating validated predictive models. In addition to the two-dimensional molecular descriptors, we include transporter inhibition predictions as descriptors and evaluate the influence of the same on the performance of the cholestasis models.