Drug Discovery Research Research Articles

CanSAR 2024-an update to the public drug discovery knowledgebase.

canSAR (https://cansar.ai) continues to serve as the largest publicly available platform for cancer-focused drug discovery and translational research. It integrates multidisciplinary data from disparate and otherwise siloed public data sources as well as data curated uniquely for canSAR. In addition, canSAR deploys a suite of curation and standardization tools together with AI algorithms to generate new knowledge from these integrated data to inform hypothesis generation. Here we report the latest updates to canSAR. As well as increasing available data, we provide enhancements to our algorithms to improve the offering to the user. Notably, our enhancements include a revised ligandability classifier leveraging Positive Unlabeled Learning that finds twice as many ligandable opportunities across the pocketome, and our revised chemical standardization pipeline and hierarchy better enables the aggregation of structurally related molecular records.

Enzymatic Biosensors: Advanced Tools for Soil and Plant Health Monitoring

Soil health is the potential of the soil to support the productivity of organisms, preserve the environment, and improve the health of plants and animals. Healthy soil is vital for agricultural sustainability and environmental resilience. The presence of high concentrations of toxic chemicals (pollutants and contaminants) in the soil poses a significant risk to human health and the ecosystem. Plant health is influenced by various factors, including the surrounding environment. To minimize constraints such as low nutrient levels, ecosystem contamination/pollution, and pest and disease incidences in crops, and to maximize productivity and ensure agricultural sustainability, advanced detection and management strategies are urgently needed. Diagnosis, prediction, and monitoring are essential for managing agricultural practices to mitigate these harmful impacts. The development of improved biosensing devices has been driven by the demand for quick, accurate, sensitive, real-time, and fast instruments for screening and identifying contaminants. Specific enzymes are used as the biological sensing element in an enzyme-based biosensor, which is combined with a transducer to transform the signal from the enzymatic reaction into a quantifiable response proportionate to the analyte concentration. Enzymatic biosensors need to possess high specificity, selectivity, reproducibility, stability, a low limit of detection, a low limit of quantification, and a rapid response time. They have a diverse array of uses, such as soil quality monitoring, water monitoring, food quality monitoring, pathogen detection, drug discovery, disease identification, and environmental research.

Core publications in drug discovery and natural product research

Natural products have long been a cornerstone of drug discovery, providing diverse and biologically relevant chemical scaffolds. This work aims to guide newcomers to natural product research and, specifically, drug discovery by presenting a curated list of 30 key publications selected through an international survey of experts and critical evaluation by the authors. The selected works span textbooks, review articles, and original research papers, covering various aspects of natural product research, including chemistry, pharmacology, analytical sciences, emerging open science, and computational approaches. We discuss historical milestones in natural product drug discovery, highlighting the specific contributions of the U.S. National Cancer Institute in developing anticancer and anti-HIV agents. The present work also addresses current challenges and innovations in the field, emphasizing the importance of data quality, interdisciplinary collaboration, and the integration of artificial intelligence. By providing this carefully selected reading list and accompanying analysis, we aim to offer a comprehensive yet accessible entry point for researchers new to natural product-based drug discovery and highlight future directions and opportunities in this dynamic field.

Structural insights into Influenza A virus RNA polymerase PB1 binding to nuclear import host factor RanBP5

The genome of influenza A viruses consists of eight RNA segments that form a heterotrimer, and the viral genome undergoes transcription and replication in the nucleus. Thus, during infection, newly synthesized RNA polymerase subunits must be imported into the nucleus. Although several models have been proposed for this process, the consensus is that the RNA polymerase subunits PB1 and PA form a dimer in the cytoplasm and are transported into the nucleus by Ran binding protein 5 (RanBP5). The PB2 subunit undergoes separate transport to complete the nuclear import. However, the molecular mechanism of nuclear import by host factors and their interactions with proteins are largely unknown. Here we present the structural analysis of the RanBP5 and PB1 NLS domain complex by cryo-EM at 3.2 Å resolution. The pattern shows that the NLS domain of PB1 does not exist in a secondary structure and interacts with RanBP5 in a wrapped state. In addition, biochemical analyses of the mutant have identified critical amino acid sites involved in complex binding. The results suggest a stepwise assembly of influenza virus structural components regulated by nuclear import mechanisms and host factor binding, with important implications for drug discovery research.

Structural Insights into Helix–Loop–Helix Peptides for “Ligand-Targeting” Intracellular Drug Delivery via VEGF Receptor-Mediated Endocytosis

As a new alternative to antibody-drug conjugates, we developed “ligand-targeting” peptide-drug conjugates (PDCs), in which conformationally constrained helix-loop-helix (HLH) peptide M49 targeting human vascular endothelial growth factor-A (VEGF) was used as a drug carrier. The biochemical study showed that HLH peptide M49 made a complex with VEGF in the extracellular environment, and then the M49/VEGF complex interacts with the receptor on the cell surface to induce cellular internalization via the endocytic pathway. Here, we present an X-ray crystal structure of the M49/VEGF complex at 1.5 Å resolution using a protein crystal grown in microgravity. The structure illustrated the “ligand-targeting” cellular uptake mechanism for intracellular drug delivery and the molecular basis on the peptide-VEGF binding mode with tight binding and high target specificity. In addition, mutational studies and thermodynamic analysis provided information on the driving forces of the complex formation. This work would contribute to the design of mid-size molecular-targeting peptides as well as HLH peptides, advancing the research in drug discovery and chemical biology.

The quick screening of binding compounds and proteins for drug discovery and pharmacological research

In drug discovery and pharmacological research, early identification of target molecules for compounds with pharmacological effects is crucial. However, this process often requires significant effort and can be rate-limiting, thereby slowing down research progress. This paper introduces a simplified and rapid method for quick screening of binding compounds or proteins. Utilizing Affinity Selection Mass Spectrometry (ASMS), this technique efficiently detects compound-target binding through size-exclusion chromatography and mass spectrometry. ASMS offers high sensitivity and specificity, making it ideal for accurate identification of binding interactions. We have further enhanced ASMS to handle membrane proteins without solubilization, creating Binder Selection Technology (BST). BST allows screening against both soluble and challenging membrane proteins such as GPCRs and SLC transporters. By using cell membrane fractions or organelle fractions with high target molecule expression, BST efficiently identifies potential binding compounds. This innovative method constructs a comprehensive database of binding compounds for various targets, facilitating rapid hypothesis testing and pharmacological evaluation. Additionally, BST screens 17,000 proteins, including membrane proteins, using wheat germ cell-free and animal cell expression systems. This approach allows exploration of binding interactions without labeling compounds or immobilizing proteins, preserving their native state. BST is powerful for identifying targets of compounds with known pharmacological effects but unknown targets in animal or cell-based assays. By utilizing BST, researchers can overcome bottlenecks in early drug discovery, significantly enhancing research speed and success rates. This method represents a major advancement, providing an efficient and effective way to identify and validate target molecules in drug discovery.

Chemical Probes Review: Choosing the Right Path Towards Pharmacological Targets in Drug Discovery, Challenges and Future Perspectives.

Chemical probes are essential for academic research and target validation for disease identification. They facilitate drug discovery, target function investigation, and translation studies. A chemical probe provides starting material that can accelerate therapeutic values and safety measures for identifying any biological target in drug discovery. Essential read outs depend on their versatility in biochemical testing, proving the hypothesis, selectivity, specificity, affinity towards the target site, and valuable in new therapeutic approaches. Disease management will depend upon chemical probes as a primitive tool to ascertain the physicochemical stability for in vivo and in vitro studies useful for clinical trials and industrial application in the future. For cancer research, bacterial infection, and neurodegenerative disorders, chemical probes are integrated circuits which are on pipeline for the drug discovery process Furthermore, pharmacological modulators incorporate activators, crosslinkers, degraders, and inhibitors. Reports accessed depend on their structural, mechanical, biochemical, and pharmacological characterization in drug discovery research. The perspective for designing any chemical probes concludes with the utilization of drug discovery and identification of the potential target. It focuses mainly on evidence-based studies and produces promising results in successfully delivering novel therapeutics to treat cancers and other disorders at the target site. Moreover, natural product pharmacophores like rapamycin, cephalosporin, and α-lactamase are utilized for drug discovery. Chemical probes revolutionize computational-based study design depending on identifying novel targets within the database framework. Chemical probes are the clinical answers for drug development and goforward tools in solving other riddles for scientists and researchers working in this industries.

Beware of N-Benzoyloxybenzamides.

Following a High-Throughput Screening campaign to discover inhibitors of acid ceramidase, we report the novel and extremely potent covalent inhibitor, 1. Following resynthesis and stability monitoring, we discovered that 1 is chemically unstable and reacts with DMSO at room temperature. This mode of decomposition is likely general for this class of compound, and we urge caution for their use in drug discovery research.

Construction of Aryl Azide‐ and Triazole‐Based Unnatural Amino Acid Motifs via the Pd(II)‐Catalyzed C(sp3)−H Arylation

AbstractThis paper reports a method for the synthesis of examples of aryl azide‐ and triazole moiety‐based unnatural amino acid motifs using the Pd(II)‐catalyzed arylation of the prochiral C(sp3)−H bonds of carboxamides of amino acids as one of the key transformations. Synthesis of racemic and enantioenriched aryl azide‐based unnatural amino acid motifs including, norvaline, leucine, norleucine, phenylalanine, 2‐aminobutyric acid, 2‐aminooctanoic acid, and non‐α‐amino acid derivatives were shown. Additionally, we attempted the synthesis of triazole moiety‐installed norvaline, leucine, norleucine, phenylalanine, 2‐aminobutyric acid, 2‐aminooctanoic acid, and non‐α‐amino acid derivatives via the Cu‐catalyzed Click reaction. We also demonstrated the removal of the protecting groups and the synthesis of free amino group‐containing aryl azide‐ or triazole‐based unnatural amino acid motifs and peptides. Generally, azide moiety‐containing unnatural amino acid motifs are important molecular tools in chemical biology. Furthermore, aryl azide‐ and triazole moiety‐containing unnatural amino acid scaffolds are notable substrates in medicinal chemistry and drug discovery research. Accordingly, this work contributes to enriching the library of aryl azide‐ and triazole‐based unnatural amino acid derivatives.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): An Emerging Tool in Radiopharmaceutical Science.

Although radioactive experiments are necessary in radiopharmaceutical drug discovery and theranostic cancer research, they are expensive, require special facilities, and face certain restrictions. Thus, finding techniques not involving radioactivity is highly beneficial for minimizing these disadvantages in such research. In this regard, methods using inductively coupled plasma-mass spectrometry (ICP-MS) have emerged as viable alternatives to traditional radioactive approaches. Despite its potential, practical applications of ICP-MS in radiopharmaceutical cancer research have only emerged in recent years. This Perspective focuses on the development and implementation of nonradioactive ICP-MS-based assays in radiopharmaceutical research and aims to inspire future research efforts in this area.

ModXNA: A Modular Approach to Parametrization of Modified Nucleic Acids for Use with Amber Force Fields.

Modified nucleic acids have surged as a popular therapeutic route, emphasizing the importance of nucleic acid research in drug discovery and development. Beyond well-known RNA vaccines, antisense oligonucleotides and aptamers can incorporate various modified nucleic acids to target specific biomolecules for various therapeutic activities. Molecular dynamics simulations can accelerate the design and development of these systems with noncanonical nucleic acids by observing intricate dynamic properties and relative stability on the all-atom level. However, modeling these modified systems is challenging due to the time and resources required to parametrize components outside default force field parameters. Here, we present modXNA, a tool to derive and build modified nucleotides for use with Amber force fields. Several nucleic acid systems varying in size and number of modification sites were used to evaluate the accuracy of modXNA parameters, and results indicate the dynamics and structure are preserved throughout the simulations. We detail the protocol for quantum mechanics charge derivation and describe a workflow for implementing modXNA in Amber molecular dynamics simulations, which includes updates and added features to CPPTRAJ.

Benzyl Alcohol Functionalization of [1.1.1]Propellane with Alkanes and Aldehydes.

Bicyclo[1.1.1]pentanes (BCPs) play a crucial role in drug discovery research as C(sp3)-rich bioisosteres of benzene rings. However, the preparation of BCPs with strong alkane C(sp3)-H bonds has not been reported to date. In this study, we reported a method for light-induced benzyl alcohol functionalization of [1.1.1]propellane with aliphatic hydrocarbons (which have not previously been explored for this purpose) and aldehydes under metal- and photocatalyst-free conditions. The BCP products could be transformed into various useful derivatives, demonstrating the utility of the method. Notably, we achieved the synthesis of functionalized BCPs with simple alkanes.

Single-cell technology for drug discovery and development

The success rate of drug development today remains low, with long development cycles and high costs, especially in areas such as oncology, neurology, immunology, and infectious diseases. Single-cell omics, encompassing transcriptomics, genomics, epigenomics, proteomics, and metabolomics enable the analysis of gene expression profiles and cellular heterogeneity from the perspective of individual cells, offering a high-resolution view of their functional diversity. These technologies can help reveal disease mechanisms, drug target identification and validation, selection of preclinical models and candidate drugs, and clinical decision-making based on disease response to drugs, all at the single-cell level. The development of deep learning technology has provided a powerful tool for research in drug discovery based on single-cell techniques, which has evolved with the advent of large-scale public databases to predict drug responses and targets. In addition, traditional Chinese medicine (TCMs) research has also entered the era of single-cell technology. Single-cell omics technologies offer an alternative way in deciphering the mechanisms of TCMs in disease treatment, revealing drug targets, screening new drugs, and designing combinations of TCMs. This review aims to explore the application of single-cell omics technologies in drug screening and development comprehensively, highlighting how they accelerate the drug development process and facilitate personalized medicine by precisely identifying therapeutic targets, predicting drug responsiveness, deciphering mechanisms of action. It is also concluded that drug development process and therapeutic efficacy of drugs can be improved by combining single-cell omics and artificial intelligence techniques.

SMCVdb: a database of experimental cellular toxicity information for drug candidate molecules.

Many drug discovery exercises fail because small molecules that are effective inhibitors of target proteins exhibit high cellular toxicity. Early and effective assessment of toxicity and pharmacokinetics is essential to accelerate the drug discovery process. Conventional methods for toxicity profiling, including in vitro and in vivo assays, are laborious and resource-intensive. In response, we introduce the Small Molecule Cell Viability Database (SMCVdb), a comprehensive resource containing toxicity data for over 24 000 compounds obtained through high-content imaging (HCI). SMCVdb seamlessly integrates chemical descriptions and molecular weight data, offering researchers a holistic platform for toxicity data aiding compound prioritization and selection based on biological and economic considerations. Data collection for SMCVdb involved a systematic approach combining HCI toxicity profiling with chemical information and quality control measures ensured data accuracy and consistency. The user-friendly web interface of SMCVdb provides multiple search and filter options, allowing users to query the database based on compound name, molecular weight range, or viability percentage. SMCVdb empowers users to access toxicity profiles, molecular weights, compound names, and chemical descriptions, facilitating the exploration of relationships between compound properties and their effects on cell viability. In summary, the database provides experimentally derived cellular toxicity information for over 24 000 drug candidate molecules to academic researchers, and pharmaceutical companies. The SMCVdb will keep growing and will prove to be a pivotal resource to expedite research in drug discovery and compound evaluation. Database URL: http://smcvdb.rcb.ac.in:4321/.

Targeted Therapy in Breast Cancer: Advantages and Advancements of Antibody-Drug Conjugates, a Type of Chemo-Biologic Hybrid Drugs.

Cancer is a significant health challenge globally, with millions of people affected every year, resulting in high morbidity and mortality. Although other treatment options are available with limitations, chemotherapy, either standalone or combined with other therapeutic procedures, is the most commonly used practice of treating cancer. In chemotherapy, cancer cells/malignant tumors are targeted; however, due to less target specificity, along with malignant cells, normal cells are also affected, which leads to various off-target effects (side effects) that impact the patient quality of life. Out of all the different types of cancers, breast cancer is the most common type of cancer in humans worldwide. Current anticancer drug discovery research aims to develop therapeutics with higher potency and lower toxicity, which is only possible through target-specific therapy. Antibody-drug conjugates (ADCs) are explicitly designed to target malignant tumors and minimize off-target effects by reducing systemic cytotoxicity. Several ADCs have been approved for clinical use and have shown moderate to good efficacy so far. Considering various aspects, chemotherapy and ADCs are useful in treating cancer. However, ADCs provide a more focused and less toxic approach, which is especially helpful in cases where resistance to chemotherapy (drug resistance) occurs and in the type of malignancies in which specific antigens are overexpressed. Ongoing ADC research aims to develop more target-specific cancer treatments. In short, this study presents a concise overview of ADCs specific to breast cancer treatment. This study provides insight into the classifications, mechanisms of action, structural aspects, and clinical trial phases (current status) of these chemo-biologic drugs (ADCs).

Novel Therapeutic Approach for Obesity: Seaweeds as an Alternative Medicine with the Latest Conventional Therapy.

The prevalence of overweight and obesity is increasing worldwide. Common comorbidities related to obesity, significantly polygenic disorders, cardiovascular disease, and heart conditions affect social and monetary systems. Over the past decade, research in drug discovery and development has opened new paths for alternative and conventional medicine. With a deeper comprehension of its underlying mechanisms, obesity is now recognized more as a chronic condition rather than merely a result of lifestyle choices. Nonetheless, addressing it solely through lifestyle changes is challenging due to the intricate nature of energy regulation dysfunction. The Federal Drug Administration (FDA) has approved six medications for the management of overweight and obesity. Seaweed are plants and algae that grow in oceans, rivers, and lakes. Studies have shown that seaweed has therapeutic potential in the management of body weight and obesity. Seaweed compounds such as carotenoids, xanthophyll, astaxanthin, fucoidans, and fucoxanthin have been demonstrated as potential bioactive components in the treatment of obesity. The abundance of natural seaweed bioactive compounds has been explored for their therapeutic potential for treating obesity worldwide. Keeping this view, this review covered the latest developments in the discovery of varied anti-obese seaweed and its bioactive components for the management of obesity.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

Identification of membrane proteins targeted by small-molecule compounds using nanomagnetic beads.

In drug discovery research, it is important to identify target proteins of bioactive small-molecule compounds and analyse their functions. In this study, we examined whether target membrane proteins could be captured by compounds that bind to membrane proteins on the cell surface. For this purpose, we performed affinity purification using the compound-immobilized nanomagnetic beads. Affinity purification with nanomagnetic beads is known to be effective for determining the protein binding partners of small molecules. However, most previous studies have targeted proteins in the cytoplasm. As a model compound, we chose BMS-1166 (a representative small-molecule compound from Bristol Myers Squibb), a PD-1/PD-L1 immune checkpoint inhibitor that binds to PD- L1 and promotes PD-L1 dimerization. BMS-1166-immobilized beads were manufactured and incubated with extracts of cells with high PD-L1 protein expression. The bound protein was confirmed by western blotting and proteomic analysis to be PD-L1. BMS-1166-immobilized nano-magnetic beads were able to specifically bind and capture the membrane protein PD-L1. In addition, high-purity protein could be obtained from cell extracts in a single step. This is the first report of the purification of a membrane protein to high purity with nanobeads. Nanomagnetic beads with immobilized compounds are an effective tool for identifying the protein binding partners of small molecules, especially when the targets are membrane proteins.

Synthesis of Coumarin-oligonucleiotide Conjugates for Application in DNA-encoded Libraries

Oligonucleotides, including DNA and RNA, can be functionalized by chemical modification based on synthetic organic chemistry. For example, ligand-oligonucleotide conjugates have a wide variety of applications. Conjugates of functional ligands and oligonucleotides have attracted attention in recent years as a drug delivery system (DDS) for improving the efficacy of oligonucleotide therapeutics. In addition, oligonucleotide conjugates with drug candidate compounds as ligands have been applied to drug screening using DNA-encoded libraries (DELs). Against this background, we have focused on the development of practical synthetic methods for ligand-oligonucleotide conjugates. Recently, we have developed a new synthetic method to construct oligonucleotides conjugated with coumarins and dipeptides, which are expected to have bioactivity, for application to DDS research of oligonucleotide therapeutics and drug discovery research using DEL. In this review, we will discuss the details, including how to construct a coumarin scaffold on oligonucleotides based on Knoevenagel condensation.

Integration of a fully automated flow cytometry system with high robustness into a Screening Station

In recent years, there has been an increasing demand for the detection of rare cells in drug discovery research, such as cells that have differentiated off-purpose or are required for immunogenicity evaluation. Since detection and quantification limits depend on the robustness of the experiment, inter-human differences in technique have a significant impact on the performance of the assay system. Here, we integrated flow cytometry into a cell experiment platform, Screening Station, to construct a robust assay system, examined each step of the flow cytometric pretreatment using Jurkat cells, and finally evaluated the overall assay performance. Cell detection rate when the experiment was performed manually was 48.8% ± 5.7% (CV=11.6%) versus 73.7%±2.0% (CV=2.8%) with the automated method. To further clarify the analytical performance of the automated method, 1-100 PD-1 expressing Jurkat cells were spiked with 1 × 105 Jurkat cells, and the lower limit of detection, linearity, and CV% were evaluated. Average detection rate was 69%, decision count was 0.985, and lower limit of detection was 4 cells (0.004%). We evaluated the CV% value of the number of detected cells per spiked cell and found our system to be highly robust, approximating a binomial distribution with a 69% recovery rate. In conclusion, we have integrated the Novocyte flow cytometry system into an automated experimental platform, Screening Station, to create a fully automated flow cytometric assay system with high robustness. Our platform can fulfill the technology needs of drug discovery for rare cell detection, which have intensified in recent years.