Search

Given the escalating incidence of bacterial diseases and the challenge posed by pathogenic bacterial resistance, it is imperative to identify appropriate methodologies for conducting proteomic investigations on bacteria, and thereby promoting the target-based drug/pesticide discovery. Interestingly, a novel technology termed "activity-based protein profiling" (ABPP) has been developed to identify the target proteins of active molecules. However, few studies have summarized advancements in ABPP for identifying the target proteins in antibacterial-active compounds. In order to accelerate the discovery and development of new drug/agrochemical discovery, we provide a concise overview of ABPP and its recent applications in antibacterial agent discovery. Diversiform cases were cited to demonstrate the potential of ABPP for target identification though highlighting the design strategies and summarizing the reported target protein of antibacterial compounds. Overall, this review is an excellent reference for probe design towards antibacterial compounds, and offers a new perspective of ABPP in bactericide development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: Natural products (NPs) play a crucial role in the development of therapeutic drugs. However, it is still highly challenging to identify the targets of NPs. Besides, NPs usually exert their pharmacological activities via acting on multiple targets or pathways, which also poses great difficulties for the target identification of NPs., Objectives: Inspired by our continuous efforts in designing drug-like protein degraders, this study introduced a successful example for the target identification and drug discovery of natural products evodiamine by employing PROTAC technology., Methods: Taking advantages of proteolysis targeting chimera (PROTAC), herein an integrated strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of antitumor NP evodiamine., Results: In this study, both highly potent PROTACs and negative controls were designed for quantitative proteomic analysis. Furthermore, REXO4 was confirmed as a direct target of 3-fluoro-10-hydroxylevodiamine, which induced cell death through ROS. In addition, the PROTAC 13c effectively degraded REXO4 both in vitro and in vivo, leading to potent antitumor activities and reduced toxic side effects., Conclusion: In summary, we developed an integrated strategy for the target identification and drug discovery of NPs, which was successfully applied to the PROTAC derivatization and target characterization of evodiamine. This proof-of-concept study highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery, exhibiting broad application in NP-based drug development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Production and hosting by Elsevier B.V.)

Objective: The rapid expansion of the biomedical literature challenges traditional review methods, especially during outbreaks of emerging infectious diseases when quick action is critical. Our study aims to explore the potential of ChatGPT to automate the biomedical literature review for rapid drug discovery., Materials and Methods: We introduce a novel automated pipeline helping to identify drugs for a given virus in response to a potential future global health threat. Our approach can be used to select PubMed articles identifying a drug target for the given virus. We tested our approach on two known pathogens: SARS-CoV-2, where the literature is vast, and Nipah, where the literature is sparse. Specifically, a panel of three experts reviewed a set of PubMed articles and labeled them as either describing a drug target for the given virus or not. The same task was given to the automated pipeline and its performance was based on whether it labeled the articles similarly to the human experts. We applied a number of prompt engineering techniques to improve the performance of ChatGPT., Results: Our best configuration used GPT-4 by OpenAI and achieved an out-of-sample validation performance with accuracy/F1-score/sensitivity/specificity of 92.87%/88.43%/83.38%/97.82% for SARS-CoV-2 and 87.40%/73.90%/74.72%/91.36% for Nipah., Conclusion: These results highlight the utility of ChatGPT in drug discovery and development and reveal their potential to enable rapid drug target identification during a pandemic-level health emergency., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Aims: Nonalcoholic steatohepatitis (NASH) is the severe subtype of nonalcoholic fatty diseases (NAFLD) with few options for treatment. Patients with NASH exhibit partial responses to the current therapeutics and adverse effects. Identification of the binding proteins for the drugs is essential to understanding the mechanism and adverse effects of the drugs and fuels the discovery of potent and safe drugs. This paper aims to critically discuss recent advances in covalent and noncovalent approaches for identifying binding proteins that mediate NASH progression, along with an in-depth analysis of the mechanisms by which these targets regulate NASH., Materials and Methods: A literature search was conducted to identify the relevant studies in the database of PubMed and the American Chemical Society. The search covered articles published from January 1990 to July 2024, using the search terms with keywords such as NASH, benzophenone, diazirine, photo-affinity labeling, thermal protein profiling, CETSA, target identification., Key Findings: The covalent approaches utilize drugs modified with diazirine and benzophenone to covalently crosslink with the target proteins, which facilitates the purification and identification of target proteins. In addition, they map the binding sites in the target proteins. By contrast, noncovalent approaches identify the binding targets of unmodified drugs in the intact cell proteome. The advantages and limitations of both approaches have been compared, along with a comprehensive analysis of recent innovations that further enhance the efficiency and specificity., Significance: The analyses of the applicability of these approaches provide novel tools to delineate NASH pathogenesis and promote drug discovery., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As a vital project of forward chemical genetic research, target deconvolution aims to identify the molecular targets of an active hit compound. Chemoproteomics, either with chemical probe-facilitated target enrichment or probe-free, provides a straightforward and effective approach to profile the target landscape and unravel the mechanisms of action. Canonical methods rely on chemical probes to enable target engagement, enrichment, and identification, whereas click chemistry and photoaffinity labeling techniques improve the efficiency, sensitivity, and spatial accuracy of target recognition. In comparison, recently developed probe-free methods detect protein-ligand interactions without the need to modify the ligand molecule. This review provides a comprehensive overview of different approaches and recent advancements for target identification and highlights the significance of chemoproteomics in investigating biological processes and advancing drug discovery processes., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Target identification is an essential part of the drug discovery and development process, and its efficacy plays a crucial role in the success of any given therapy. Although protein target identification research can be challenging, two main approaches can help researchers make significant discoveries: affinity-based pull-down and label-free methods. Affinity-based pull-down methods use small molecules conjugated with tags to selectively isolate target proteins, while label-free methods utilize small molecules in their natural state to identify targets. Target identification strategy selection is essential to the success of any drug discovery process and must be carefully considered when determining how to best pursue a specific project. This paper provides an overview of the current target identification approaches in drug discovery related to experimental biological assays, focusing primarily on affinity-based pull-down and label-free approaches, and discusses their main limitations and advantages., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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., Competing Interests: Declaration of interests We declare competing interests. The authors who were affiliated with Takeda Development Center Americas were employees of Takeda Pharmaceuticals during the course of this work, and have real or potential ownership interest in Takeda. AEC serves on the Scientific Advisory Board of, and has ownership interest in, Recursion, a pharmaceutical company using image-based profiling for drug discovery. Authors from the Broad Institute of Harvard and MIT were funded by a grant from Takeda for part of this work., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives., Competing Interests: The authors declare no conflict of interest.

Introduction: As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named ' Patrimony' was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence., Areas Covered: Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates., Expert Opinion: We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods-comparative genomics and network-based methods-for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery., (© 2021. The Author(s).)

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and experimental approach is formulated on a conceptual framework in which network biology is used to bridge between individual molecular entities and the cellular phenotype that emerges when those entities interact in a network. Multiple aspects of early stage discovery are addressed including the data-driven elucidation of biological processes implicated in disease, target identification and validation, phenotypic discovery of active molecules and their mechanism of action, and extraction of genetic target support from human population genetics data. Validation is described via summary of a number of discovery projects and details from a project aimed at COVID-19 disease., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Genetic screening technologies to identify and validate macromolecular interactions (MMIs) essential for complex pathways remain an important unmet need for systems biology and therapeutics development. Here, we use a library of peptides from diverse prokaryal genomes to screen MMIs promoting the nuclear relocalization of Forkhead Box O3 (FOXO3a), a tumor suppressor more frequently inactivated by post-translational modification than mutation. A hit peptide engages the 14-3-3 family of signal regulators through a phosphorylation-dependent interaction, modulates FOXO3a-mediated transcription, and suppresses cancer cell growth. In a crystal structure, the hit peptide occupies the phosphopeptide-binding groove of 14-3-3ε in a conformation distinct from its natural peptide substrates. A biophysical screen identifies drug-like small molecules that displace the hit peptide from 14-3-3ε, providing starting points for structure-guided development. Our findings exemplify "protein interference," an approach using evolutionarily diverse, natural peptides to rapidly identify, validate, and develop chemical probes against MMIs essential for complex cellular phenotypes., Competing Interests: Declaration of interests A.R.V., C.J.T., and G.J.M. are founders of PhoreMost Ltd. C.J.T., M.M., and M.C. are employees and shareholders of the company, A.R.V. is a shareholder and the chief scientific adviser, and G.J.M. and B.S.H. are shareholders. P.M.W. is a founder and shareholder, and N.M. a shareholder, of PYC Therapeutics., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

With their wide repertoire of mechanisms, antimicrobial peptides (AMPs) are promising alternatives to fight against varied pathogenic microorganisms (bacteria, fungi, viruses, parasites, etc.). AMPs, novel components of the innate immune defense system, are secreted by all organisms. The aquatic environment represents a huge population and an enormous source of varied AMPs. Polyphemusin-I, a marine AMP isolated from hemocytes of an American horseshoe crab, possesses high antimicrobial activities. Studies on polyphemusin-I have verified the intracellular mechanisms of action, however, its intracellular targets are not yet explored. In this study, we employed Escherichia coli proteome microarrays to systematically screen the entire intracellular protein targets of polyphemusin-I. A total of 97 protein targets of polyphemusin-I were statistically analyzed from the quadruplicate Escherichia coli proteome microarrays assays. Among these identified protein targets, 56 proteins had cellular location inside the cell (i.e., cytoplasm), one in the plasma membrane, one in the periplasm and the rest 39 proteins had no specified cellular location. The bioinformatics analysis of these identified protein targets of polyphemusin-I in gene ontology (GO) enrichment category of molecular function revealed significant enrichment in nucleic acid related GO terms i.e., "RNA binding", "nucleotide binding", "nuclease activities", "uracil DNA N-glycosylase activities" and others. Moreover, enrichment in GO category of biological process also depicted enrichment in nucleic acid related GO terms, such as "nucleic acid phosphodiester bond hydrolysis", "deoxyribonucleotide metabolism", and others. In accordance to GO enrichment analysis, protein families (PFAM) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis also showed significant enrichment in nucleic acid terms. These enrichment results suggest that polyphemusin-I targets nucleic acid-associated proteins. Furthermore, to provide a comprehensive study, we compared the identified protein targets of polyphemusin-I with previously identified protein targets of four AMPs (P-Der, Lfcin B, PR-39, and Bac 7) using Escherichia coli proteome microarrays. The comparison study of five AMPs (polyhemusin-I, P-Der, Lfcin B, PR-39, and Bac 7) showed only nine common protein targets in all the five AMPs, whereas a total of 39 and 43 common protein targets were identified among the two marine AMPs (polyphemusin-I and P-Der) and three terrestrial AMPs (Lfcin B, PR-39 and Bac7), respectively. To further reveal the target pattern of marine and terrestrial AMPs, the enrichment results obtained from common protein targets of marine AMPs with terrestrial AMPs were compared. The comparison result indicated that AMPs have unique mechanism of action among marine or terrestrial AMPs. Hence, in this study, we have not only identified the intracellular protein targets of polyphemusin-I, but also revealed the protein target differences between marine AMPs and terrestrial AMPs.

Emerging Plasmodium parasite drug resistance is threatening progress towards malaria control and elimination. While recent efforts in cell-based, high-throughput drug screening have produced first-in-class drugs with promising activities against different Plasmodium life cycle stages, most of these antimalarial agents have elusive mechanisms of action. Though challenging to address, target identification can provide valuable information to facilitate lead optimization and preclinical drug prioritization. Recently, proteome-wide methods for direct assessment of drug-protein interactions have emerged as powerful tools in a number of systems, including Plasmodium. In this review, we will discuss current chemoproteomic strategies that have been adapted to antimalarial drug target discovery, including affinity- and activity-based protein profiling and the energetics-based techniques thermal proteome profiling and stability of proteins from rates of oxidation. The successful application of chemoproteomics to the Plasmodium blood stage highlights the potential of these methods to link inhibitors to their molecular targets in more elusive Plasmodium life stages and intracellular pathogens in the future., (© 2021 Wiley-VCH GmbH.)

Host-microbiota interactions are vital for diverse pathophysiological events and may be targeted for innovative therapeutics. Nuclear receptors (NRs) are versatile host sensors of microbial signals that coordinate diverse environmental cues with local and remote adaptions. Harnessing NR-mediated sensory machinery could provide an alternative lynchpin for gut microbiota-oriented drug discovery strategy., Competing Interests: Declaration of Interests The authors declare no conflict of interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Phenotypic screening for bioactive small molecules is typically combined with affinity-based chemical proteomics to uncover the respective molecular targets. However, such assays and the explored bioactivity are biased toward the monitored phenotype, and target identification often requires chemical derivatization of the hit compound. In contrast, unbiased cellular profiling approaches record hundreds of parameters upon compound perturbation to map bioactivity in a broader biological context and may link a profile to the molecular target or mode of action. Herein we report the discovery of the diaminopyrimidine DP68 as a Sigma 1 (σ 1 ) receptor antagonist by combining morphological profiling using the Cell Painting assay and thermal proteome profiling. Our results highlight that integration of complementary profiling approaches may enable both detection of bioactivity and target identification for small molecules., Competing Interests: Declaration of interests The authors declare no competing interest., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Electrophilic natural products (ENPs) are a rich source of bioactive molecules with tremendous therapeutic potential. While their synthetic complexity may hinder their direct use as therapeutics, they represent tools for elucidation of suitable molecular targets and serve as inspiration for the design of simplified synthetic counterparts. Here, we review the recent use of various activity-based protein profiling methods to uncover molecular targets of ENPs. Beyond target identification, these examples also showcase further development of synthetic ligands from natural product starting points. Two examples demonstrate how ENPs can progress the emerging fields of targeted protein degradation and molecular glues. Though challenges still remain in the synthesis of ENP-based probes, and in their synthetic simplification, their potential for discovery of novel mechanisms of action makes it well worth the effort., Competing Interests: Declaration of Interests N.L. is on the scientific advisory board of Totus medicines and Monte Rosa Therapeutics. P.G. is an employee of Merck KGaA., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases' statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

Phenotype-based screening has emerged as an alternative route for discovering new chemical entities toward first-in-class therapeutics. However, clarifying their mode of action has been a significant bottleneck for drug discovery. For target protein identification, conventionally bioactive small molecules are conjugated onto solid supports and then applied to isolate target proteins from whole proteome. This approach requires a high binding affinity between bioactive small molecules and their target proteins. Besides, the binding affinity can be significantly hampered after structural modifications of bioactive molecules with linkers. To overcome these limitations, two major strategies have recently been pursued: (1) the covalent conjugation between small molecules and target proteins using photoactivatable moieties or electrophiles, and (2) label-free target identification through monitoring target engagement by tracking the thermal, proteolytic, or chemical stability of target proteins. This review focuses on recent advancements in target identification from covalent capturing to label-free strategies., Competing Interests: Declaration of interests The authors declare no competing interests. S.B.P. is a founder and CEO of SPARK Biopharma, Inc. S.B.P. is an associate editor of RSC Chemical Biology, and an advisory board member of ACS Chemical Biology, Molecules, Frontiers in Chemistry, Bulletin of the Korean Chemical Society, and Methods and Application of in Fluorescence. S.B.P is also a scientific advisory committee member of the Institut Pasteur Korea (IPK), Handok Pharmaceutical, AmorePacific, and a chair of the global council of the International Chemical Biology Society (ICSB)., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Tau protein aggregates are a recognized neuropathological feature in Alzheimer's disease as well as many other neurodegenerative disorders, known as tauopathies. The development of tau-targeting therapies is therefore extremely important but efficient strategies or protein targets are still unclear. Here, we performed a cell-based phenotypic screening under endoplasmic reticulum (ER) stress conditions and identified a small molecule, SB1617, capable of suppressing abnormal tau protein aggregation. By applying label-free target identification technology, we revealed that the transient enhancement of protein kinase-like endoplasmic reticulum kinase (PERK) signaling pathway through the inhibition of stress-responsive SB1617 targets, PDIA3 and DNAJC3, is an effective strategy for regulating proteostasis in tauopathies. The molecular mechanism and the promising efficacy of SB1617 were demonstrated in neuronal cells and a mouse model with traumatic brain injury, a tauopathy known to involve ER stress., (© 2020 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Natural products (NPs) have been an important source of therapeutic drugs in clinic use and contributed many chemical probes for research. The usefulness of NPs is however often marred by the incomplete understanding of their direct cellular targets. A number of experimental methods for drug target identification have been developed over the years. One class of methods, termed "label-free" methodology, exploits the energetic and biophysical features accompanying the association of macromolecules with drugs and other compounds in their native forms. Herein we review the working principles, assay implementations, and key applications of the most important approaches, and also give examples where they have been applied to NPs. We also assess the key advantages and limitations of each method. Furthermore, we address when and how the label-free methodology can be particularly useful considering some of the unique features of NP chemistry and bioactivation., Competing Interests: Declaration of Competing Interest P.N. is the inventor of patents related to the CETSA method and is the cofounder of Pelago Biosciences AB. The other authors declare that there are no conflicts of interest., (Copyright © 2020. Published by Elsevier Inc.)

Discovery of small-molecule inducers of unique phenotypic changes combined with subsequent target identification often provides new insights into cellular functions. Here, we applied integrated profiling based on cellular morphological and proteomic changes to compound screening. We identified an indane derivative, NPD9055, which is mechanistically distinct from reference compounds with known modes of action. Employing a chemical proteomics approach, we then showed that NPD9055 binds subunits of heterotrimeric G-protein G i . An in vitro [ 35 S]GTPγS-binding assay revealed that NPD9055 inhibited GDP/GTP exchange on a Gα i subunit induced by a G-protein-coupled receptor agonist, but not on another G-protein from the Gα s family. In intact HeLa cells, NPD9055 induced an increase in intracellular Ca 2+ levels and ERK/MAPK phosphorylation, both of which are regulated by Gβγ, following its dissociation from Gα i . Our observations suggest that NPD9055 targets Gα i and thus regulates Gβγ-dependent cellular processes, most likely by causing the dissociation of Gβγ from Gα i .

Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any healing agent. An antiviral drug is urgently required for dengue treatment. Some potential antiviral agents are still in the process of drug discovery, but the development of more effective active molecules is in critical demand. Herein, we aimed to provide an efficient technique for target prediction using homopharma and network-based methods, which is reliable and expeditious to hunt for the possible human targets of three phenolic lipids (anarcardic acid, cardol, and cardanol) related to dengue viral (DENV) infection as a case study. Using several databases, the similarity search and network-based analyses were applied on the three phenolic lipids resulting in the identification of seven possible targets as follows. Based on protein annotation, three phenolic lipids may interrupt or disturb the human proteins, namely KAT5, GAPDH, ACTB, and HSP90AA1, whose biological functions have been previously reported to be involved with viruses in the family Flaviviridae. In addition, these phenolic lipids might inhibit the mechanism of the viral proteins: NS3, NS5, and E proteins. The DENV and human proteins obtained from this study could be potential targets for further molecular optimization on compounds with a phenolic lipid core structure in anti-dengue drug discovery. As such, this pipeline could be a valuable tool to identify possible targets of active compounds.

Activity-based protein profiling (ABPP) and bioimaging have been developed in recent years as powerful technologies in drug discovery. Specifically, both approaches can be applied in critical steps of drug development, such as therapy target discovery, high-throughput drug screening and target identification of bioactive molecules. We have been focused on the development of various strategies that enable simultaneous activity-based protein profiling and bioimaging studies, thus facilitating an understanding of drug actions and potential toxicities. In this Minireview, we summarize these novel strategies and applications, with the aim of promoting these technologies in drug discovery., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The unique nature of microgravity encountered in space provides an opportunity for drug discovery and development that cannot be replicated on Earth. From the production of superior protein crystals to the identification and validation of new drug targets to microarray analyses of transcripts attenuated by microgravity, there are numerous examples which demonstrate the benefit of exploiting the space environment. Moreover, studies conducted on Space Shuttle missions, the International Space Station and other craft have had a direct benefit for drug development programmes such as those directed against reducing bone and muscle loss or increasing bone formation. This review will highlight advances made in both drug discovery and development and offer some future insight into how drug discovery and associated technologies may be further advanced using the microgravity assist., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

The number of drugs available for treatment of active tuberculosis is diminishing due to increased multidrug resistance selection in Mycobacterium tuberculosis leading to multiple (MDR) and extensively (XDR) resistant strains. Also, TB is treated with multiple drugs to minimize further resistance development, mandating a sustained effort to identify new lead compounds for treating drug-resistant TB and shortening time to cure for all TB infections. High throughput screening, a well-known approach to discovery of new leads, is conducted in two basic modes 1) using whole cells and screening for inhibition of growth, or whole cell reporter cells that signal when a specific pathway is perturbed, and 2) in vitro non-cell based enzyme or other functional assays for direct ligand-target binding. Combining high throughput screening for inhibitors of growth (to identify and chemically assess inhibitors active on whole cells), followed by target identification abrogates the problem of discovering new leads in non-cell based systems that are inactive on whole cells due to issues with target access (e.g., uptake). High throughput screening of 341,778 compounds by the National Institutes of Health identified 8,950 primary hit, growth inhibitors of M. tuberculosis. Final evaluation based on reproducibility, potency, medicinal chemistry inspection, and cytotoxicity on tissue culture cells identified 1,113 priority compounds. These data were deposited in PubChem, making data available to TB research labs for follow up studies on target identification. This effort led to the identification of compounds targeting Pks13, MmpL3, DprE1, AspS, EthA, GuaB2, nonreplicating cells, and VKOR (Vitamin K epoxide reductase)., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Affinity selection (AS)-MS is a label-free binding assay technology for the analysis of interactions between targets and small drug molecules, which does not require modification of targets or compounds. AS-MS technology has been used in drug discovery research for more than 10 years, and is currently one of the most important affinity-based screening techniques. As such, it may be the driving force for novel small molecule drug discovery. This review introduces the principles of AS-MS technology and its use in high-throughput screening (HTS), then discusses strategies for its use in drug discovery and its application in target identification.

Target identification for biologically active small molecules remains a major barrier for drug discovery. Cancer cells exhibiting defective DNA mismatch repair (dMMR) have been used as a forward genetics system to uncover compound targets. However, this approach has been limited by the dearth of cancer cell lines that harbor naturally arising dMMR. Here, we establish a platform for forward genetic screening using CRISPR/Cas9 to engineer dMMR into mammalian cells. We demonstrate the utility of this approach to identify mechanisms of drug action in mouse and human cancer cell lines using in vitro selections against three cellular toxins. In each screen, compound-resistant alleles emerged in drug-resistant clones, supporting the notion that engineered dMMR enables forward genetic screening in mammalian cells., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Common approaches to antibiotic discovery include small-molecule screens for growth inhibition in target pathogens and screens for inhibitors of purified enzymes. These approaches have a shared intent of seeking to directly target a vital Achilles heel in a pathogen of interest. Here, we report the first screen against a sporulation pathway in a non-pathogenic bacterium as a means of discovering novel antibiotics-this effort has resulted in two important discoveries. First, we show that the sporulation program of Streptomyces venezuelae is exquisitely sensitive to numerous forms of DNA damage. Second, we have identified a DNA gyrase inhibitor. This molecule, EN-7, is active against pathogenic species that are resistant to ciprofloxacin and other clinically important antibiotics. We suggest that this strategy could be applied to other morphogenetic pathways in prokaryotes or eukaryotes as a means of identifying novel chemical matter having scientific and clinical utility., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Complex diseases (e.g., Alzheimer's disease) or infectious diseases are usually caused by complicated and varied factors, including environmental and genetic factors. Multi-target (polypharmacology) drugs have been suggested and have emerged as powerful and promising alternative paradigms in modern medicinal chemistry for the development of versatile chemotherapeutic agents to solve these medical challenges. The multifunctional agents capable of modulating multiple biological targets simultaneously display great advantages of higher efficacy, improved safety profile, and simpler administration compared to single-targeted agents. Therefore, multifunctional agents would certainly open novel avenues to rationally design the next generation of more effective but less toxic therapeutic agents. Herein, the authors review the recent progress made in the discovery and design processes of selective multi-targeted agents, especially the successful application of multi-target drugs for the treatment of Alzheimer's disease., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.

Natural products have long played a leading role as direct source of drugs or as a means to inspire informed molecular design. Indeed, natural products have been biologically prevalidated as protein-binding motifs by millions of years of evolutionary pressure. Despite the tailored architectures, and the ever-growing chemistry toolbox to aid access such privileged structures, identifying the modes of action by which these molecules can be harnessed as therapeutics remains a major bottleneck in discovery chemistry. Herein, an overview of cheminformatics methods applied to the identification of modes of action of natural products is given, and a discussion of successful case studies is provided. A special focus is given to machine learning methods that may help to streamline the development of natural products into drug leads.

Target identification (target-ID) is an important step in elucidating the mechanisms of action of bioactive small molecules. In the past few decades, a number of target-ID methods have been developed. Among these, affinity labeling has been reliably used for specific modifications, as well as for the identification of weakly interacting protein targets, membrane-associated protein targets, and target-interacting proteins under native cellular conditions, which are generally difficult to achieve by conventional pull-down methods. In general, affinity labeling utilizes chemical probes composed of a bioactive small molecule, a reactive group, and a detection unit. However, the design and synthesis of highly functionalized chemical probes is often time-consuming. To address this issue, we have recently developed some simple affinity labeling methods using small fluorogenic tags, such as 4-alkoxy-7-nitro-2,1,3-benzoxadiazole (O-NBD), 2,3-dichloromaleimide (diCMI), and 4-azidophthalimide (AzPI), and successfully achieved the specific fluorescent labeling of target proteins, even in living cells. These methods should be useful for target-ID in phenotypic drug discovery.

Many small molecule compounds with anticancer activities are discovered through phenotype-based screens. However, discovering the targets of these small molecules has been challenging. The gold standard for target identification requires the discovery of mutations in the target protein that block the effects of small molecules in vitro as well as in vivo. Here we describe the procedures for isolating drug resistant clones using the colorectal cancer cell line HCT-116 followed by whole-exome sequencing to identify recurrent mutations associated with compound resistance. Together with downstream in vitro and in vivo validation experiments, this strategy enables rapid target discovery for cytotoxic compounds.

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Computational prediction of the clinical success or failure of a potential drug target for therapeutic use is a challenging problem. Novel network propagation algorithms that integrate heterogeneous biological networks are proving useful for drug target identification and prioritization. These approaches typically utilize a network describing relationships between targets, a method to disseminate the relevant information through the network, and a method to elucidate new associations between targets and diseases. Here, we utilize one such network propagation-based approach, DTINet, which starts with diffusion component analysis of networks of both potential drug targets and diseases. Then an inductive matrix completion algorithm is applied to identify novel disease targets based on their network topological similarities with known disease targets with successfully launched drugs. DTINet performed well as assessed with area under the precision-recall curve (AUPR = 0.88 ± 0.007) and area under the receiver operating characteristic curve (AUROC = 0.86 ± 0.008). These metrics improved when we combined data from multiple networks in the target space but reduced significantly when we used a more conservative method to define negative controls (AUPR = 0.56 ± 0.007, AUROC = 0.57 ± 0.007). We are optimistic that integration of more relevant and cleaner datasets and networks, careful calibration of model parameters, as well as algorithmic improvements will improve prediction accuracy. However, we also recognize that predicting drug targets that are likely to be successful is an extremely challenging problem due to its complex nature and sparsity of known disease targets.

Identification of the protein targets of bioactive small molecules is a routine challenge in chemical biology and phenotype-based drug discovery. Recent years have seen an explosion of approaches to meeting this challenge, but the traditional method of affinity pulldowns remains a practical choice in many contexts. This technique can be used as long as an affinity probe can be synthesized, usually with a crosslinking moiety to enable photo-affinity pulldowns. It can be applied to varied tissue types and can be performed with minimal specialized equipment. Here, we provide our protocol for photo-affinity pulldown experiments, with notes on making this method generally applicable to varied target identification challenges., (© 2019 Elsevier Inc. All rights reserved.)

The identification of protein targets and the elucidation of the molecular mechanism of action (MMoA) of bioactive small molecules are central goals of chemical biology. Many different techniques for target identification and engagement are developed, but none of them is generic. Here we describe one of these techniques-the cellular thermal shift assay (CETSA). The assay works without any labeling of proteins or small molecules, which allows the investigation of the unaltered interaction between the interaction partners. Briefly, the influence of small molecules on the thermal stability of proteins within whole cell lysates is investigated. We describe this approach in two variants: the conventional immunoblot-based approach (CETSA), as well as an unbiased approach based on a proteome-wide mass spectrometric analysis (thermal proteome profiling, TPP). The CETSA approach requires preknowledge about possible target proteins and can only detect a few proteins at once. Although TPP is technically more demanding, it allows for the identification of multiple (off)targets without any preknowledge.

Background: The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands., Results: Utilizing the data available in the Open Targets platform, potential G-protein coupled receptor and endogenous ligand disease association pairs were systematically identified. Intriguing examples such as GPR35 for inflammatory bowel disease and CXCR4 for viral infection are used as illustrations of how a systematic approach can aid in the prioritization of interesting drug discovery hypotheses. Combining evidences for G-protein coupled receptors and their corresponding endogenous peptidergic ligands increases confidence and provides supportive evidence for potential new target-disease hypotheses. Comparing such hypotheses to the global pharma drug discovery pipeline to validate the approach showed that more than 93% of G-protein coupled receptor-disease pairs with a high overall Open Targets score involved receptors with an existing drug discovery program., Conclusions: The Open Targets gene-disease score can be used to prioritize potential G-protein coupled receptors-indication hypotheses. In addition, availability of multiple different evidence types markedly increases confidence as does combining evidence from known receptor-ligand pairs. Comparing the top-ranked hypotheses to the current global pharma pipeline serves validation of our approach and identifies and prioritizes new therapeutic opportunities.

Background: Target identification is necessary for the comprehensive inference of the mechanism of action of a compound. The application of computational methods to predict the targets of bioactive compounds saves cost and time in drug research and development. Therefore, we designed an integrated strategy consisting of ligand-protein docking, network analysis, enrichment analysis, and an experimental surface plasmon resonance (SPR) method to identify and validate new targets, and then used enriched pathways to elucidate the underlying pharmacological mechanisms. Here, we used rhein, a compound with various pharmacological activities, as an example to find some of its previously unknown targets and to determine its pharmacological activity., Results: A total of nine candidate targets were discovered, including LCK, HSP90AA1, RAB5A, EGFR, CDK2, CDK6, GSK3B, p38, and JNK. LCK was confirmed through SPR experiments, and HSP90AA1, EGFR, CDK6, p38, and JNK were validated through previous reports. Rhein network regulations are complex and interconnected. The therapeutic effect of rhein is the synergistic and comprehensive result of this vast and complex network, and the perturbation of multiple targets gives rhein its various pharmacological activities., Conclusions: This study provided a new integrated strategy to identify new targets of bioactive compounds and reveal their molecular mechanisms of action.

After decades of relative inactivity, a large increase in efforts to discover antitubercular therapeutics has brought insights into the biology of Mycobacterium tuberculosis (Mtb) and promising new drugs such as bedaquiline, which inhibits ATP synthase, and the nitroimidazoles delamanid and pretomanid, which inhibit both mycolic acid synthesis and energy production. Despite these advances, the drug discovery pipeline remains underpopulated. The field desperately needs compounds with novel mechanisms of action capable of inhibiting multi- and extensively drug -resistant Mtb (M/XDR-TB) and, potentially, nonreplicating Mtb with the hope of shortening the duration of required therapy. New knowledge about Mtb, along with new methods and technologies, has driven exploration into novel target areas, such as energy production and central metabolism, that diverge from the classical targets in macromolecular synthesis. Here, we review new small molecule drug candidates that act on these novel targets to highlight the methods and perspectives advancing the field. These new targets bring with them the aspiration of shortening treatment duration as well as a pipeline of effective regimens against XDR-TB, positioning Mtb drug discovery to become a model for anti-infective discovery.

Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Defects in adipocyte function associated with obesity drive the development of systemic insulin resistance and type 2 diabetes. Agents that correct obesity-linked adipocyte dysfunction serve as useful insulin sensitizers in humans, as is exemplified by the thiazolidinediones (TZDs). We have developed a new platform that integrates advanced chemoproteomics with phenotypic screening to identify small molecules that promote differentiation and lipid storage in adipocytes, and, in tandem, their molecular target(s). These molecules mimic the activity of TZDs in culture and thus may also serve as insulin sensitizers in vivo. Central to this platform is the use of fully functionalized fragment (FFF) probes that consist of a variable, fragment-like recognition element linked to an alkyne-diazirine group that enables the photoactivated capture of probe-bound proteins directly in living cells and subsequent copper-catalyzed azide-alkyne cycloaddition to reporter tags for enrichment and identification of these probe-bound proteins by mass spectrometry. This platform, which can be adapted to diverse screens and cell types beyond adipocytes, has the potential to uncover new biological pathways amenable to pharmacological modulation that may impact human disease.