Applications of chemogenomic library screening in drug discovery

Ube3a unsilencer for the potential treatment of Angelman syndrome

Deletion of the maternal UBE3A allele causes Angelman syndrome (AS); because paternal UBE3A is epigenetically silenced by a long non-coding antisense (UBE3A-ATS) in neurons, this nearly eliminates UBE3A protein in the brain. Reactivating paternal UBE3A holds promise for treating AS. We previously showed topoisomerase inhibitors can reactivate paternal UBE3A, but their therapeutic challenges prompted our search for small molecule unsilencers with a different mechanism of action. Here, we found that (S)-PHA533533 acts through a novel mechanism to significantly increase paternal Ube3a mRNA and UBE3A protein levels while downregulating Ube3a-ATS in primary neurons derived from AS model mice. Furthermore, peripheral delivery of (S)-PHA533533 in AS model mice induces widespread neuronal UBE3A expression. Finally, we show that (S)-PHA533533 unsilences paternal UBE3A in AS patient-derived neurons, highlighting its translational potential. Our findings provide a lead for developing a small molecule treatment for AS that could be safe, non-invasively delivered, and capable of brain-wide unsilencing of paternal UBE3A.

Monensin Suppresses Multiple Features of House Dust Mite-Induced Experimental Asthma in Mice

Despite intense efforts to develop efficient therapeutic regimes for asthma, there is a large demand for novel treatment strategies in this disease. Here we evaluated the impact of monensin, a drug with potent anti-mast cell effects, in a mouse model of asthma. Allergic airway inflammation was induced by sensitization of mice with house dust mite (HDM) antigen, and effects of monensin on airway hyperreactivity and inflammatory parameters were studied. Following intraperitoneal administration, monensin did not suppress airway hyperreactivity but was shown to have anti-inflammatory properties, as manifested by reduced eosinophil- and lymphocyte infiltration into the airway lumen, and by suppressed inflammation of the lung tissue. After intranasal instillation, monensin exhibited similar anti-inflammatory effects as seen after intraperitoneal administration. Moreover, intranasally administered monensin was demonstrated to suppress goblet cell hyperplasia, and to cause a reduction in the expression of genes coding for key inflammatory markers. Further, monensin blocked mast cell degranulation in the airways of allergen-sensitized mice. Together, this study reveals that monensin has the capacity to suppress key pathological events associated with allergic airway inflammation.

Chemogenomics for NR1 nuclear hormone receptors

Nuclear receptors (NRs) regulate transcription in response to ligand binding and NR modulation allows pharmacological control of gene expression. Although some NRs are relevant as drug targets, the NR1 family, which comprises 19 NRs binding to hormones, vitamins, and lipid metabolites, has only been partially explored from a translational perspective. To enable systematic target identification and validation for this protein family in phenotypic settings, we present an NR1 chemogenomic (CG) compound set optimized for complementary activity/selectivity profiles and chemical diversity. Based on broad profiling of candidates for specificity, toxicity, and off-target liabilities, sixty-nine comprehensively annotated NR1 agonists, antagonists and inverse agonists covering all members of the NR1 family and meeting potency and selectivity standards are included in the final NR1 CG set. Proof-of-concept application of this set reveals effects of NR1 members in autophagy, neuroinflammation and cancer cell death, and confirms the suitability of the set for target identification and validation.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Therapeutic Peptides, Proteins and their Nanostructures for Drug Delivery and Precision Medicine

Peptide and protein nanostructures with tunable structural features, multifunctionality, biocompatibility and biomolecular recognition capacity enable development of efficient targeted drug delivery tools for precision medicine applications. In this review article, we present various techniques employed for the synthesis and self-assembly of peptides and proteins into nanostructures. We discuss design strategies utilized to enhance their stability, drug-loading capacity, and controlled release properties, in addition to the mechanisms by which peptide nanostructures interact with target cells, including receptor-mediated endocytosis and cell-penetrating capabilities. We also explore the potential of peptide and protein nanostructures for precision medicine, focusing on applications in personalized therapies and disease-specific targeting for diagnostics and therapeutics in diseases such as cancer.

Development of Selective Pyrido[2,3-d]pyrimidin-7(8H)-one-Based Mammalian STE20-Like (MST3/4) Kinase Inhibitors

Mammalian STE20-like (MST) kinases 1-4 play key roles in regulating the Hippo and autophagy pathways, and their dysregulation has been implicated in cancer development. In contrast to the well-studied MST1/2, the roles of MST3/4 are less clear, in part due to the lack of potent and selective inhibitors. Here, we re-evaluated literature compounds, and used structure-guided design to optimize the p21-activated kinase (PAK) inhibitor G-5555 (8) to selectively target MST3/4. These efforts resulted in the development of MR24 (24) and MR30 (27) with good kinome-wide selectivity and high cellular potency. The distinct cellular functions of closely related MST kinases can now be elucidated with subfamily-selective chemical tool compounds using a combination of the MST1/2 inhibitor PF-06447475 (2) and the two MST3/4 inhibitors developed. We found that MST3/4-selective inhibition caused a cell-cycle arrest in the G1 phase, whereas MST1/2 inhibition resulted in accumulation of cells in the G2/M phase.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

Fluorescent Reporter Systems to Investigate Chromatin Effector Proteins in Living Cells

Epigenetic research faces the challenge of the high complexity and tight regulation in chromatin modification networks. Although many isolated mechanisms of chromatin-mediated gene regulation have been described, solid approaches for the comprehensive analysis of specific processes as parts of the bigger epigenome network are missing. In order to expand the toolbox of methods by a system that will help to capture and describe the complexity of transcriptional regulation, we describe here a robust protocol for the generation of stable reporter systems for transcriptional activity and summarize their applications. The system allows for the induced recruitment of a chromatin regulator to a fluorescent reporter gene, followed by the detection of transcriptional changes using flow cytometry. The reporter gene is integrated into an endogenous chromatin environment, thus enabling the detection of regulatory dependencies of the investigated chromatin regulator on endogenous cofactors. The system allows for an easy and dynamic readout at the single-cell level and the ability to compensate for cell-to-cell variances of transcription. The modular design of the system enables the simple adjustment of the method for the investigation of different chromatin regulators in a broad panel of cell lines. We also summarize applications of this technology to characterize the silencing velocity of different chromatin effectors, removal of activating histone modifications, analysis of stability and reversibility of epigenome modifications, the investigation of the effects of small molecule on chromatin effectors and of functional effector-coregulator relationships. The presented method allows to investigate the complexity of transcriptional regulation by epigenetic effector proteins in living cells.

Birinapant selectively enhances immunotoxin-mediated killing of cancer cells conditional on the IAP protein levels within target cells

Immunotoxins (ITs) target cancer cells via antibody binding to surface antigens followed by internalization and toxin-mediated inhibition of protein synthesis. The fate of cells responding to IT treatment depends on the amount and stability of specific pro-apoptotic and pro-survival proteins. When treated with a pseudomonas exotoxin-based immunotoxin (HB21PE40), the triple-negative breast cancer (TNBC) cell line MDA-MB-468 displayed a notable resistance to toxin-mediated killing compared to the epidermoid carcinoma cell line, A431, despite succumbing to the same level of protein synthesis inhibition. In a combination screen of ~1912 clinically relevant and mechanistically annotated compounds, we identified several agents that greatly enhanced IT-mediated killing of MDA-MB-468 cells while exhibiting only a modest enhancement for A431 cells. Of interest, two Smac mimetics, birinapant and SM164, exhibited this kind of differential enhancement. To investigate the basis for this, we probed cells for the presence of inhibitor of apoptosis (IAP) proteins and monitored their stability after the addition of immunotoxin. We found that high levels of IAPs inhibited immunotoxin-mediated cell death. Further, TNFα levels were not relevant for the combination's efficacy. In tumor xenograft studies, combinations of immunotoxin and birinapant caused complete regressions in MDA-MB-468tumor-bearing mice but not in mice with A431 tumors. We propose that IAPs constitute a barrier to immunotoxin efficacy which can be overcome with combination treatments that include Smac mimetics.

Unraveling the Interaction of Diflunisal with Cyclodextrin and Lysozyme by Fluorescence Spectroscopy

Understanding the interaction between the drug:carrier complex and protein is essential for the development of a new drug-delivery system. However, the majority of reports are based on an understanding of interactions between the drug and protein. Here, we present our findings on the interaction of the anti-inflammatory drug diflunisal with the drug carrier cyclodextrin (CD) and the protein lysozyme, utilizing steady-state and time-resolved fluorescence spectroscopy. Our findings reveal a different pattern of molecular interaction between the inclusion complex of β-CD (β-CD) or hydroxypropyl-β-CD (HP-β-CD) (as the host) and diflunisal (as the guest) in the presence of protein lysozyme. The quantum yield for the 1:2 guest:host complex is twice that of the 1:1 guest:host complex, indicating a more stable hydrophobic microenvironment created in the 1:2 complex. Consequently, the nonradiative decay pathway is significantly reduced. The interaction is characterized by ultrafast solvation dynamics and time-resolved fluorescence resonance energy transfer. The solvation dynamics of the lysozyme becomes 10% faster under the condition of binding with the drug, indicating a negligible change in the polar environment after binding. In addition, the fluorescence lifetime of diflunisal (acceptor) is increased by 50% in the presence of the lysozyme (donor), which indicates that the drug molecule is bound to the binding pocket on the surface of the protein, and the average distance between active tryptophan in the hydrophobic region and diflunisal is calculated to be approximately 50 Å. Excitation and emission matrix spectroscopy reveals that the tryptophan emission increases 3-5 times in the presence of both diflunisal and CD. This indicates that the tryptophan of lysozyme may be present in a more hydrophobic environment in the presence of both diflunisal and CD. Our observations on the interaction of diflunisal with β-CD and lysozyme are well supported by molecular dynamics simulation. Results from this study may have an impact on the development of a better drug-delivery system in the future. It also reveals a fundamental molecular mechanism of interaction of the drug-carrier complex with the protein.

Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.

Transcriptomics-Guided In Silico Drug Repurposing: Identifying New Candidates with Dual-Stage Antiplasmodial Activity

In tropical and subtropical areas, malaria stands as a profound public health challenge, causing an estimated 247 million cases worldwide annually. Given the absence of a viable vaccine, the timely and effective treatment of malaria remains a critical priority. However, the growing resistance of parasites to currently utilized drugs underscores the critical need for the identification of new antimalarial therapies. Here, we aimed to identify potential new drug candidates against Plasmodium falciparum, the main causative agent of malaria, by analyzing the transcriptomes of different life stages of the parasite and identifying highly expressed genes. We searched for genes that were expressed in all stages of the parasite's life cycle, including the asexual blood stage, gametocyte stage, liver stage, and sexual stages in the insect vector, using transcriptomics data from publicly available databases. From this analysis, we found 674 overlapping genes, including 409 essential ones. By searching through drug target databases, we discovered 70 potential drug targets and 75 associated bioactive compounds. We sought to expand this analysis to similar compounds to known drugs. So, we found a list of 1557 similar compounds, which we predicted as actives and inactives using previously developed machine learning models against five life stages of Plasmodium spp. From this analysis, two compounds were selected, and the reactions were experimentally evaluated. The compounds HSP-990 and silvestrol aglycone showed potent inhibitory activity at nanomolar concentrations against the P. falciparum 3D7 strain asexual blood stage. Moreover, silvestrol aglycone exhibited low cytotoxicity in mammalian cells, transmission-blocking potential, and inhibitory activity comparable to those of established antimalarials. These findings warrant further investigation of silvestrol aglycone as a potential dual-acting antimalarial and transmission-blocking candidate for malaria control.

AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion

Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

Design and Synthesis of New Anthranyl Phenylhydrazides: Antileishmanial Activity and Structure-Activity Relationship

Leishmaniasis is a neglected tropical disease affecting millions of people worldwide. A centenary approach to antimonial-based drugs was first initiated with the synthesis of urea stibamine by Upendranath Brahmachari in 1922. The need for new drug development led to resistance toward antimoniates. New drug development to treat leishmaniasis is urgently needed. In this way, searching for new substances with antileishmanial activity, we synthesized ten anthranyl phenylhydrazide and three quinazolinone derivatives and evaluated them against promastigotes and the intracellular amastigotes of Leishmania amazonensis. Three compounds showed good activity against promastigotes 1b, 1d, and 1g, with IC50 between 1 and 5 μM. These new phenylhydrazides were tested against Leishmania arginase, but they all failed to inhibit this parasite enzyme, as we have shown in a previous study. To explain the possible mechanism of action, we proposed the enzyme PTR1 as a new target for these compounds based on in silico analysis. In conclusion, the new anthranyl hydrazide derivatives can be a promising scaffold for developing new substances against the protozoa parasite.

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells

Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.

Novel therapeutic strategies targeting UCP2 in uterine leiomyosarcoma

Uterine leiomyosarcoma (ULMS) is a malignant stromal tumor arising from the myometrium with a poor prognosis and very limited response to current chemotherapy. This study aimed to identify novel targets for ULMS through a three-step screening process using a chemical library consisting of 1271 Food and Drug Administration-approved drugs. First, we evaluated their inhibitory effects on ULMS cells and identified four candidates: proscillaridin A, lanatoside C, floxuridine, and digoxin. Then, we subcutaneously or orthotopically transplanted SK-UT-1 cells into mice to establish mouse models. In vivo analyses showed that proscillaridin A and lanatoside C exerted a superior antitumor effect. The results of mRNA sequencing showed that uncoupling protein 2 (UCP2) was suppressed in the sirtuin signaling pathway, increasing reactive oxygen species (ROS) and inducing cell death. Moreover, the downregulation of UCP2 induced ROS and suppressed ULMS cell growth. Furthermore, analyses using clinical samples showed that UCP2 expression was significantly upregulated in ULMS tissues than in myoma tissues both at the RNA and protein levels. These findings suggested that UCP2 is a potential therapeutic target and can contribute to the development of novel therapeutic strategies in patients with ULMS.

Drug library screening identifies histone deacetylase inhibition as a novel therapeutic strategy for choriocarcinoma

BACKGROUND

Choriocarcinoma is a rare and aggressive gynecological malignancy. The standard treatment is systemic chemotherapy as choriocarcinoma exhibits high chemosensitivity. However, refractory choriocarcinoma exhibits chemoresistance; thus, the prognosis remains very poor. This study aimed to identify novel therapeutic agents for choriocarcinoma by utilizing a drug repositioning strategy.

METHODS

Three choriocarcinoma cell lines (JAR, JEG-3, and BeWo) and a human extravillous trophoblast cell line (HTR-8/SVneo) were used for the analyses. The growth inhibitory effects of 1,271 FDA-approved compounds were evaluated in vitro screening assays and selected drugs were tested in tumor-bearing mice. Functional analyses of drug effects were performed based on RNA sequencing.

RESULTS

Muti-step screening identified vorinostat, camptothecin (S, +), topotecan, proscillaridin A, and digoxin as exhibiting an anti-cancer effect in choriocarcinoma cells. Vorinostat, a histone deacetylase inhibitor, was selected as a promising candidate for validation and the IC50 values for choriocarcinoma cells were approximately 1 μM. RNA sequencing and subsequent pathway analysis revealed that the ferroptosis pathway was likely implicated, and key ferroptosis-related genes (i.e., GPX4, NRF2, and SLC3A2) were downregulated following vorinostat treatment. Furthermore, vorinostat repressed tumor growth and downregulated the expression of GPX4 and NRF2 in JAR cell-bearing mice model.

CONCLUSION

Vorinostat, a clinically approved drug for the treatment of advanced primary cutaneous T-cell lymphoma, showed a remarkable anticancer effect both in vitro and in vivo by regulating the expression of ferroptosis-related genes. Therefore, vorinostat may be an effective therapeutic candidate for patients with choriocarcinoma.

An Introduction to Chemogenomics

Chemogenomics is an innovative approach in chemical biology that synergizes combinatorial chemistry and genomic and proteomic biology to systematically study the response of a biological system to a set of compounds, which can aid the identification and validation of biological targets as well as biologically active small-molecule agents responsible for a phenotypic outcome. Central to this strategy is a collection of chemically diverse compounds, a so-called chemogenomics library. Selection and annotation of vastly available chemogenomic compound candidates for an inclusion in such set present a challenge, but optimal compound selection is critical for success of chemogenomics. The library can be used in a wide variety of research applications from biological mechanism deconvolution to drug discovery. However, phenotypic screening methods are typically required to be high-throughput and equipped with a systematic analysis of complex biological-chemical interactions. This chapter provides a general outline to the chemogenomics approach, including concept and critical steps in all stages of this innovative chemical biology strategy.

Compilation of Custom Compound/Bioactivity Datasets from Public Repositories

Public repositories containing compound-bioactivity data for millions of small molecules offer a valuable resource for chemogenomic compound candidate search. Nonetheless, owning to nonuniform data mining, these databases are often incomplete, thus advocating the combined use of data from several repositories to increase target coverage and data accuracy. Here, we present a workflow to generate custom datasets from public databases for mining chemogenomic compound candidates. The compiled set provides flags for differences in structural and bioactivity data and enables rapid extraction of potent and selective bioactive compounds.

Developing a Kinase Chemogenomic Set: Facilitating Investigation into Kinase Biology by Linking Phenotypes to Targets

Advances in increasingly complex phenotypic screening with lower throughput have necessitated the screening of smaller more highly annotated sets. One such collection of compounds which has been recently assembled is the kinase chemogenomic set. This is a set of curated kinase inhibitors built upon previous iterations, PKIS and PKIS2, and donations from our partners. Each compound in the set has been carefully selected based on selectivity, potency, and kinome coverage. These compounds as a set have been made available to the scientific community, enabling phenotypic screens to identify kinases that drive novel biology. Additionally, the associated data deposited in the public domain have also been used to inform new inhibitor design. Further expansion of this set to complete kinome coverage will allow for a greater understanding of kinase biology and its role in disease.

Advances in high-throughput mass spectrometry in drug discovery

High-throughput (HT) screening drug discovery, during which thousands or millions of compounds are screened, remains the key methodology for identifying active chemical matter in early drug discovery pipelines. Recent technological developments in mass spectrometry (MS) and automation have revolutionized the application of MS for use in HT screens. These methods allow the targeting of unlabelled biomolecules in HT assays, thereby expanding the breadth of targets for which HT assays can be developed compared to traditional approaches. Moreover, these label-free MS assays are often cheaper, faster, and more physiologically relevant than competing assay technologies. In this review, we will describe current MS techniques used in drug discovery and explain their advantages and disadvantages. We will highlight the power of mass spectrometry in label-free in vitro assays, and its application for setting up multiplexed cellular phenotypic assays, providing an exciting new tool for screening compounds in cell lines, and even primary cells. Finally, we will give an outlook on how technological advances will increase the future use and the capabilities of mass spectrometry in drug discovery.

Discovery of AHCY as an Off-Target of Doxorubicin by Integrative Analysis of Photoaffinity Labeling Chemoproteomics and Untargeted Metabolomics

Target identification is critically important for understanding the mechanism of action of drugs. Here, we reported a new strategy for deconvolution of drug targets (or off-targets) with photoaffinity labeling chemoproteomics in combination with untargeted metabolomics by using doxorubicin (DOX) as a model. The DOX-derived photoaffinity probes were prepared and applied to capture DOX-interacting proteins in living cells. The captured DOX-interacting proteins were then identified by label-free quantitative proteomics. Totally, 151 significant proteins were identified with high confidence (fold change >4, p-value < 0.005). The gene ontology enrichment analysis suggested that the proteins were mainly involved in carbon metabolism, citrate cycle, fatty acid metabolism, and metabolic pathways. Therefore, untargeted metabolomics was applied to quantify the significantly altered metabolites in cells upon drug treatment. The pathway enrichment analysis suggested that DOX mainly interrupted with the processes of pyrimidine and purine metabolism, carbon metabolism, methionine metabolism, and phosphatidylcholine biosynthesis. Integrative analysis of chemoproteomics and metabolomics indicated that adenosylhomocysteinase (AHCY) is a new target (off-target) of DOX leading to the accumulation of S-adenosyl homocysteine. This deduced DOX target was confirmed by the cellular thermal shift assay, affinity competitive pull-down assay, biochemical assay, and siRNA knock down experiments. Our result suggested that AHCY is the uncovered off-target of DOX.

An open source plant kinase chemogenomics set

One hundred twenty-nine protein kinases, selected to represent the diversity of the rice (Oryza sativa) kinome, were cloned and tested for expression in Escherichia coli. Forty of these rice kinases were purified and screened using differential scanning fluorimetry (DSF) against 627 diverse kinase inhibitors, with a range of structures and activities targeting diverse human kinases. Thirty-seven active compounds were then tested for their ability to modify primary root development in Arabidopsis. Of these, 14 compounds caused a significant reduction of primary root length compared with control plants. Two of these inhibitory compounds bind to the predicted orthologue of Arabidopsis PSKR1, one of two receptors for PSK, a small sulfated peptide that positively controls root development. The reduced root length phenotype could not be rescued by the exogenous addition of the PSK peptide, suggesting that chemical treatment may inhibit both PSKR1 and its closely related receptor PSKR2. Six of the compounds acting as root growth inhibitors in Arabidopsis conferred the same effect in rice. Compound RAF265 (CHIR-265), previously shown to bind the human kinase BRAF (B-Raf proto-oncogene, serine/threonine kinase), also binds to nine highly conserved rice kinases tested. The binding of human and rice kinases to the same compound suggests that human kinase inhibitor sets will be useful for dissecting the function of plant kinases.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Identification of active natural products that induce lysosomal biogenesis by lysosome-based screening and biological evaluation

Lysosomal biogenesis is an essential adaptive process by which lysosomes exert their function in maintaining cellular homeostasis. Defects in lysosomal enzymes and functions lead to lysosome-related diseases, including lysosomal storage diseases and neurodegenerative disorders. Thus, activation of the autophagy-lysosomal pathway, especially induction of lysosomal biogenesis, might be an effective strategy for the treatment of lysosome-related diseases. In this study, we established a lysosome-based screening system to identify active compounds from natural products that could promote lysosomal biogenesis. The subcellular localizations of master transcriptional regulators of lysosomal genes, TFEB, TFE3 and ZKSCAN3 were examined to reveal the potential mechanisms. More than 200 compounds were screened, and we found that Hdj-23, a triterpene isolated from Walsura cochinchinensis, induced lysosomal biogenesis via activation of TFEB/TFE3. In summary, this study introduced a lysosome-based live cell screening strategy to identify bioactive compounds that promote lysosomal biogenesis, which would provide potential candidate enhancers of lysosomal biogenesis and novel insight for treating lysosome-related diseases.

Copper(I)-Catalyzed Cross-Coupling of 1-Bromoalkynes with N-Heterocyclic Organozinc Reagents

Nitrogen-containing heterocycles represent the majority of FDA-approved small-molecule pharmaceuticals. Herein, we describe a synthetic method to produce saturated N-heterocyclic drug scaffolds with an internal alkyne for elaboration. The treatment of N,N-dimethylhydrazinoalkenes with Et₂Zn, followed by a Cu(I)-catalyzed cross-coupling with 1-bromoalkynes, results in piperidines and pyrrolidines with a good yield. Five examples are reported and a proposed mechanism for the Cu(I)-catalyzed cross-coupling is presented.

Host Kinase CSNK2 is a Target for Inhibition of Pathogenic SARS-like β-Coronaviruses

Inhibition of the protein kinase CSNK2 with any of 30 specific and selective inhibitors representing different chemotypes, blocked replication of pathogenic human, bat, and murine β-coronaviruses. The potency of in-cell CSNK2A target engagement across the set of inhibitors correlated with antiviral activity and genetic knockdown confirmed the essential role of the CSNK2 holoenzyme in β-coronavirus replication. Spike protein endocytosis was blocked by CSNK2A inhibition, indicating that antiviral activity was due in part to a suppression of viral entry. CSNK2A inhibition may be a viable target for the development of anti-SARS-like β-coronavirus drugs.

Image-Based Annotation of Chemogenomic Libraries for Phenotypic Screening

Phenotypical screening is a widely used approach in drug discovery for the identification of small molecules with cellular activities. However, functional annotation of identified hits often poses a challenge. The development of small molecules with narrow or exclusive target selectivity such as chemical probes and chemogenomic (CG) libraries, greatly diminishes this challenge, but non-specific effects caused by compound toxicity or interference with basic cellular functions still pose a problem to associate phenotypic readouts with molecular targets. Hence, each compound should ideally be comprehensively characterized regarding its effects on general cell functions. Here, we report an optimized live-cell multiplexed assay that classifies cells based on nuclear morphology, presenting an excellent indicator for cellular responses such as early apoptosis and necrosis. This basic readout in combination with the detection of other general cell damaging activities of small molecules such as changes in cytoskeletal morphology, cell cycle and mitochondrial health provides a comprehensive time-dependent characterization of the effect of small molecules on cellular health in a single experiment. The developed high-content assay offers multi-dimensional comprehensive characterization that can be used to delineate generic effects regarding cell functions and cell viability, allowing an assessment of compound suitability for subsequent detailed phenotypic and mechanistic studies.

Modulating intracellular pathways to improve non-viral delivery of RNA therapeutics

RNA therapeutics (e.g. siRNA, oligonucleotides, mRNA, etc.) show great potential for the treatment of a myriad of diseases. However, to reach their site of action in the cytosol or nucleus of target cells, multiple intra- and extracellular barriers have to be surmounted. Several non-viral delivery systems, such as nanoparticles and conjugates, have been successfully developed to meet this requirement. Unfortunately, despite these clear advances, state-of-the-art delivery agents still suffer from relatively low intracellular delivery efficiencies. Notably, our current understanding of the intracellular delivery process is largely oversimplified. Gaining mechanistic insight into how RNA formulations are processed by cells will fuel rational design of the next generation of delivery carriers. In addition, identifying which intracellular pathways contribute to productive RNA delivery could provide opportunities to boost the delivery performance of existing nanoformulations. In this review, we discuss both established as well as emerging techniques that can be used to assess the impact of different intracellular barriers on RNA transfection performance. Next, we highlight how several modulators, including small molecules but also genetic perturbation technologies, can boost RNA delivery by intervening at differing stages of the intracellular delivery process, such as cellular uptake, intracellular trafficking, endosomal escape, autophagy and exocytosis.

Target 2035 - update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.

Host kinase CSNK2 is a target for inhibition of pathogenic β-coronaviruses including SARS-CoV-2

Inhibition of the protein kinase CSNK2 with any of 30 specific and selective inhibitors representing different chemotypes, blocked replication of pathogenic human and murine β-coronaviruses. The potency of in-cell CSNK2A target engagement across the set of inhibitors correlated with antiviral activity and genetic knockdown confirmed the essential role of the CSNK2 holoenzyme in β-coronavirus replication. Spike protein uptake was blocked by CSNK2A inhibition, indicating that antiviral activity was due in part to a suppression of viral entry. CSNK2A inhibition may be a viable target for development of new broad spectrum anti-β-coronavirus drugs.

Cheminformatic analysis of natural product-based drugs and chemical probes

Covering: 1981 to 2019Natural products continue to play a major role in drug discovery, with half of new chemical entities based structurally on a natural product. Herein, we report a cheminformatic analysis of the structural and physicochemical properties of natural product-based drugs in comparison to top-selling brand-name synthetic drugs, and a selection of chemical probes recently discovered from diversity-oriented synthesis libraries. In this analysis, natural product-based drugs covered a broad range of chemical space based on size, polarity, and three-dimensional structure. Natural product-based structures were also more prevalent in top-selling drugs of 2018 compared to 2006. Further, the drugs clustered well according to biosynthetic origins, but less so based on therapeutic classes. Macrocycles occupied distinctive and relatively underpopulated regions of chemical space, while chemical probes largely overlapped with synthetic drugs. This analysis highlights the continued opportunities to leverage natural products and their pharmacophores in modern drug discovery.

Use of AD Informer Set compounds to explore validity of novel targets in Alzheimer's disease pathology

Introduction

A chemogenomic set of small molecules with annotated activities and implicated roles in Alzheimer's disease (AD) called the AD Informer Set was recently developed and made available to the AD research community: https://treatad.org/data‐tools/ad‐informer‐set/.

Methods

Small subsets of AD Informer Set compounds were selected for AD‐relevant profiling. Nine compounds targeting proteins expressed by six AD‐implicated genes prioritized for study by Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT‐AD) teams were selected for G‐protein coupled receptor (GPCR), amyloid beta (Aβ) and tau, and pharmacokinetic (PK) studies. Four non‐overlapping compounds were analyzed in microglial cytotoxicity and phagocytosis assays.

Results

The nine compounds targeting CAPN2, EPHX2, MDK, MerTK/FLT3, or SYK proteins were profiled in 46 to 47 primary GPCR binding assays. Human induced pluripotent stem cell (iPSC)‐derived neurons were treated with the same nine compounds and secretion of Aβ peptides (Aβ40 and Aβ42) as well as levels of phosphophorylated tau (p‐tau, Thr231) and total tau (t‐tau) peptides measured at two concentrations and two timepoints. Finally, CD1 mice were dosed intravenously to determine preliminary PK and/or brain‐specific penetrance values for these compounds. As a final cell‐based study, a non‐overlapping subset of four compounds was selected based on single‐concentration screening for analysis of both cytotoxicity and phagocytosis in murine and human microglia cells.

Discussion

We have demonstrated the utility of the AD Informer Set in the validation of novel AD hypotheses using biochemical, cellular (primary and immortalized), and in vivo studies. The selectivity for their primary targets versus essential GPCRs in the brain was established for our compounds. Statistical changes in tau, p‐tau, Aβ40, and/or Aβ42 and blood–brain barrier penetrance were observed, solidifying the utility of specific compounds for AD. Single‐concentration phagocytosis results were validated as predictive of dose–response findings. These studies established workflows, validated assays, and illuminated next steps for protein targets and compounds.

Development of a chemogenomics library for phenotypic screening

With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.

Diversity-Oriented Synthesis: Amino Acetophenones as Building Blocks for the Synthesis of Natural Product Analogs

Diversity-Oriented Synthesis (DOS) represents a strategy to obtain molecule libraries with diverse structural features starting from one common compound in limited steps of synthesis. During the last two decades, DOS has become an unmissable strategy in organic synthesis and is fully integrated in various drug discovery processes. On the other hand, natural products with multiple relevant pharmacological properties have been extensively investigated as scaffolds for ligand-based drug design. In this article, we report the amino dimethoxyacetophenones that can be easily synthesized and scaled up from the commercially available 3,5-dimethoxyaniline as valuable starting blocks for the DOS of natural product analogs. More focus is placed on the synthesis of analogs of flavones, coumarins, azocanes, chalcones, and aurones, which are frequently studied as lead compounds in drug discovery.

A direct-to-biology high-throughput chemistry approach to reactive fragment screening

Methods for rapid identification of chemical tools are essential for the validation of emerging targets and to provide medicinal chemistry starting points for the development of new medicines. Here, we report a screening platform that combines 'direct-to-biology' high-throughput chemistry (D2B-HTC) with photoreactive fragments. The platform enabled the rapid synthesis of >1000 PhotoAffinity Bits (HTC-PhABits) in 384-well plates in 24 h and their subsequent screening as crude reaction products with a protein target without purification. Screening the HTC-PhABit library with carbonic anhydrase I (CAI) afforded 7 hits (0.7% hit rate), which were found to covalently crosslink in the Zn2+ binding pocket. A powerful advantage of the D2B-HTC screening platform is the ability to rapidly perform iterative design-make-test cycles, accelerating the development and optimisation of chemical tools and medicinal chemistry starting points with little investment of resource.

Chemical Probes for Understudied Kinases: Challenges and Opportunities

Over 20 years after the approval of the first-in-class protein kinase inhibitor imatinib, the biological function of a significant fraction of the human kinome remains poorly understood while most research continues to be focused on few well-validated targets. Given the strong genetic evidence for involvement of many kinases in health and disease, the understudied fraction of the kinome holds a large and unexplored potential for future therapies. Specific chemical probes are indispensable tools to interrogate biology enabling proper preclinical validation of novel kinase targets. In this Perspective, we highlight recent case studies illustrating the development of high-quality chemical probes for less-studied kinases and their application in target validation. We spotlight emerging techniques and approaches employed in the generation of chemical probes for protein kinases and beyond and discuss the associated challenges and opportunities.

Discovery of quinuclidine modulators of cellular progranulin

Phenotypic screening of an annotated small molecule library identified the quinuclidine tetrahydroisoquinoline solifenacin (1) as a robust enhancer of progranulin secretion with single digit micromolar potency in a murine microglial (BV-2) cell line. Subsequent SAR development led to the identification of 29 with a 38-fold decrease in muscarinic receptor antagonist activity and a 10-fold improvement in BV-2 potency.

The Use of Informer Sets in Screening: Perspectives on an Efficient Strategy to Identify New Probes

Small-molecule discovery typically involves large-scale screening campaigns, spanning multiple compound collections. However, such activities can be cost- or time-prohibitive, especially when using complex assay systems, limiting the number of compounds tested. Further, low hit rates can make the process inefficient. Sparse coverage of chemical structure or biological activity space can lead to limited success in a primary screen and represents a missed opportunity by virtue of selecting the "wrong" compounds to test. Thus, the choice of screening collections becomes of paramount importance. In this perspective, we discuss the utility of generating "informer sets" for small-molecule discovery, and how this strategy can be leveraged to prioritize probe candidates. While many researchers may assume that informer sets are focused on particular targets (e.g., kinases) or processes (e.g., autophagy), efforts to assemble informer sets based on historical bioactivity or successful human exposure (e.g., repurposing collections) have shown promise as well. Another method for generating informer sets is based on chemical structure, particularly when the compounds have unknown activities and targets. We describe our efforts to screen an informer set representing a collection of 100,000 small molecules synthesized through diversity-oriented synthesis (DOS). This process enables researchers to identify activity early and more extensively screen only a few chemical scaffolds, rather than the entire collection. This elegant and economic outcome is a goal of the informer set approach. Here, we aim not only to shed light on this process, but also to promote the use of informer sets more widely in small-molecule discovery projects.

Human amyotrophic lateral sclerosis excitability phenotype screen: Target discovery and validation

Drug development is hampered by poor target selection. Phenotypic screens using neurons differentiated from patient stem cells offer the possibility to validate known and discover novel disease targets in an unbiased fashion. To identify targets for managing hyperexcitability, a pathological feature of amyotrophic lateral sclerosis (ALS), we design a multi-step screening funnel using patient-derived motor neurons. High-content live cell imaging is used to evaluate neuronal excitability, and from a screen against a chemogenomic library of 2,899 target-annotated compounds, 67 reduce the hyperexcitability of ALS motor neurons carrying the SOD1(A4V) mutation, without cytotoxicity. Bioinformatic deconvolution identifies 13 targets that modulate motor neuron excitability, including two known ALS excitability modulators, AMPA receptors and Kv7.2/3 ion channels, constituting target validation. We also identify D2 dopamine receptors as modulators of ALS motor neuron excitability. This screen demonstrates the power of human disease cell-based phenotypic screens for identifying clinically relevant targets for neurological disorders.

Motor neuron hyperexcitability is observed in both ALS patients and their iPSC-derived neurons. Combining a high-content live imaging excitability phenotypic assay, high-throughput screening against a cross-annotated chemogenomic library, and bioinformatic enrichment analysis, Huang et al. identify targets modulating the hyperexcitability of ALS patient-derived motor neurons in an unbiased manner.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

The Promise and Peril of Chemical Probe Negative Controls

Chemical probes are selective modulators that are used in cell assays to link a phenotype to a gene and have become indispensable tools to explore gene function and discover therapeutic targets. Chemical probe off-targets are a confounding factor as the observed phenotype may be driven by inhibition of an unknown off-target instead of the targeted protein. A negative control, a close chemical analog of the chemical probe that is inactive against the intended target, is typically used to verify that the phenotype is indeed driven by the targeted protein. Here, we compare the selectivity profiles of four unrelated chemical probes and their respective negative controls. We find that controls that chemically deviate from the probe by a single heavy atom can be inactive against up to 80% of known off-targets if the chemical modification has a charge-neutralizing effect. In such cases, a loss in phenotype upon treatment with the negative control may be driven by loss of inhibition of an off-target. To expand this analysis, we inspect the crystal structures of 90 pairs of unrelated proteins, where both proteins within each pair is in complex with the same drug-like ligand. We computationally estimate that in 50% of cases, methylation of the ligand (a simple chemical modification often used to generate negative controls) at a position that will preclude binding to one protein (the intended target) will also preclude binding to the other (the off-target). These results emphasize the need to select negative controls with care and profile both chemical probes and negative controls against diverse protein arrays to verify that off-targets of probes are also hit by negative controls. When available, a best practice should be to verify that two unrelated chemical probes targeting the same protein elicit the same phenotype.

A genome-scale CRISPR Cas9 dropout screen identifies synthetically lethal targets in SRC-3 inhibited cancer cells

Steroid receptor coactivator 3 (SRC-3/NCoA3/AIB1), is a key regulator of gene transcription and it plays a central role in breast cancer (BC) tumorigenesis, making it a potential therapeutic target. Beyond its function as an important regulator of estrogen receptor transcriptional activity, SRC-3 also functions as a coactivator for a wide range of other transcription factors, suggesting SRC-3 inhibition can be beneficial in hormone-independent cancers as well. The recent discovery of a potent SRC-3 small molecule inhibitor, SI-2, enabled the further development of additional related compounds. SI-12 is an improved version of SI-2 that like SI-2 has anti-proliferative activity in various cancer types, including BC. Here, we sought to identify gene targets, that when inhibited in the presence of SI-12, would lead to enhanced BC cell cytotoxicity. We performed a genome-scale CRISPR-Cas9 screen in MCF-7 BC cells under conditions of pharmacological pressure with SI-12. A parallel screen was performed with an ER inhibitor, fulvestrant, to shed light on both common and distinct activities between SRC-3 and ERα inhibition. Bearing in mind the key role of SRC-3 in tumorigenesis of other types of cancer, we extended our study by validating potential hits identified from the MCF-7 screen in other cancer cell lines.

Melting point prediction of organic molecules by deciphering the chemical structure into a natural language

Establishing quantitative structure-property relationships for the rational design of small molecule drugs at the early discovery stage is highly desirable. Using natural language processing (NLP), we proposed a machine learning model to process the line notation of small organic molecules, allowing the prediction of their melting points. The model prediction accuracy benefits from training upon different canonicalized SMILES forms of the same molecules and does not decrease with increasing size, complexity, and structural flexibility. When a combination of two different canonicalized SMILES forms is used to train the model, the prediction accuracy improves. Largely distinguished from the previous fragment-based or descriptor-based models, the prediction accuracy of this NLP-based model does not decrease with increasing size, complexity, and structural flexibility of molecules. By representing the chemical structure as a natural language, this NLP-based model offers a potential tool for quantitative structure-property prediction for drug discovery and development.

Unveiling the Targets Involved in the Quest of Antileishmanial Leads Using In silico Methods

BACKGROUND

Leishmaniasis is a neglected tropical disease associated with several clinical manifestations, including cutaneous, mucocutaneous, and visceral forms. As currently available drugs have some limitations (toxicity, resistance, among others), the target-based identification has been an important approach to develop new leads against leishmaniasis. The present study aims to identify targets involved in the pharmacological action of potent antileishmanial compounds.

METHODS

The literature information regarding molecular interactions of antileishmanial compounds studied over the past half-decade is discussed. The information was obtained from databases such as Wiley, SciFinder, Science Direct, National Library of Medicine, American Chemical Society, Scientific Electronic Library Online, Scopus, Springer, Google Scholar, Web of Science, etc. Results: Numerous in vitro antileishmanial compounds showed affinity and selective interactions with enzymes such as arginase, pteridine reductase 1, trypanothione reductase, pyruvate kinase, among others, which are crucial for the survival and virulence of the Leishmania parasite.

CONCLUSION

The in-silico activity of small molecules (enzymes, proteins, among others) might be used as pharmacological tools to develop candidate compounds for the treatment of leishmaniasis. As some pharmacologically active compounds may act on more than one target, additional studies of the mechanism (s) of action of potent antileishmanial compounds might help to better understand their pharmacological action. Also, the optimization of promising antileishmanial compounds might improve their biological activity.

A Comprehensive Summary of the Knowledge on COVID-19 Treatment

Currently, the world is challenged by the coronavirus disease 2019 (COVID-19) pandemic. Epidemiologists and researchers worldwide are invariably trying to understand and combat this precarious new disease. Scrutinizing available drug options and developing potential new drugs are urgent needs to subdue this pandemic. Several intervention strategies are being considered and handled worldwide with limited success, and many drug candidates are yet in the trial phase. Despite these limitations, the development of COVID-19 treatment strategies has been accelerated to improve the clinical outcome of patients with COVID-19, and some countries have efficiently kept it under control. Recently, the use of natural and traditional medicine has also set the trend in coronavirus treatment. This review aimed to discuss the prevailing COVID-19 treatment strategies available globally by examining their efficacy, potential mechanisms, limitations, and challenges in predicting a future potential treatment candidate and bridging them with the effective traditional Chinese medicine (TCM). The findings might enrich the knowledge on traditional alternative medication and its complementary role with Western medicine in managing the COVID-19 epidemic.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.