Global mapping of pharmacological space

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.     

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.     

Deep within the filarial genome: progress of the filarial genome project.

Four years ago, a WHO/United Nations Development Programme/World Bank-sponsored genome project to study the filarial lymphatic nematode parasite Brugia malayi was initiated. The project took as its aims gene discovery for drug target and vaccine candidate identification, genome mapping, dissemination of genomic data to the world community and training of endemic country partners in genomic research. In this article, the principal investigators in the laboratories behind the project describe the background to the project, the data now emerging and goals for the future. Open access to filarial genome data is emphasized.     

Actives from MMV Open Access Boxes? A suggested way forward

It is estimated that more than 1 billion people across the world are affected by a neglected tropical disease (NTD) that requires medical intervention. These diseases tend to afflict people in areas with high rates of poverty and cost economies billions of dollars every year. Collaborative drug discovery efforts are required to reduce the burden of these diseases in endemic regions. The release of “Open Access Boxes” is an initiative launched by Medicines for Malaria Venture (MMV) in collaboration with its partners to catalyze new drug discovery in neglected diseases. These boxes are mainly requested by biology researchers across the globe who may not otherwise have access to compounds to screen nor knowledge of the workflow that needs to be followed after identification of actives from their screening campaigns. Here, we present guidelines on how to move such actives beyond the hit identification stage, to help in capacity strengthening and enable a greater impact of the initiative.     

Challenges and solutions in proteomics

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.     

Functional cell-based uHTS in chemical genomic drug discovery

The availability of genomic information significantly increases the number of potential targets available for drug discovery, although the function of many targets and their relationship to disease is unknown. In a chemical genomic research approach, ultra-high throughput screening (uHTS) of genomic targets takes place early in the drug discovery process, before target validation. Target-selective modulators then provide drug leads and pharmacological research tools to validate target function. Effective implementation of a chemical genomic strategy requires assays that can perform uHTS for large numbers of genomic targets. Cell-based functional assays are capable of the uHTS throughput required for chemical genomic research, and their functional nature provides distinct advantages over ligand-binding assays in the identification of target-selective modulators.     

Chemical genomics: massively parallel technologies for rapid lead identification and target validation

Chemical genomics is a new research paradigm with importantapplications in drug discovery. It links genomic targets withsmall-molecule chemistries thereby allowing for efficient targetvalidation and lead compound identification. ACADIA'schemical-genomics platform consists of a large and diverse small-moleculelibrary (800,000), a reference drug library (2,000), druggablegenomic targets (>300) and a cell-based functional assaytechnology (R-SAT^TM; Receptor Selection and AmplificationTechnology) that allows for ultra-high throughput screening(>500,000 data points/week) as well as high throughputpharmacology and profiling over a wide range of targets. Twoexamples are presented that illustrate the success of ourchemical-genomics approach: (i) The validation of inverse agonismat serotonin 5-HT_2A receptors as an antipsychotic mechanismand the subsequent discovery of potent and selectively acting 5-HT_2A inverse agonists, currently in preclinical development,and (ii) the discovery of the first ectopically binding subtype-selective muscarinic m1 agonist.     

Genomic approaches to drug discovery

Considerable progress has been made in exploiting the enormous amount of genomic and genetic information for the identification of potential targets for drug discovery and development. New tools that incorporate pathway information have been developed for gene expression data mining to reflect differences in pathways in normal and disease states. In addition, forward and reverse genetics used in a high-throughput mode with full-length cDNA and RNAi libraries enable the direct identification of components of signaling pathways. The discovery of the regulatory function of microRNAs highlights the importance of continuing the investigation of the genome with sophisticated tools. Furthermore, epigenetic information including DNA methylation and histone modifications that mediate important biological processes add to the possibilities to identify novel drug targets and patient populations that will benefit from new therapies.     

Modulation of neuropathic-pain-related behaviour by the spinal endocannabinoid/endovanilloid system

Neuropathic pain refers to chronic pain that results from injury to the nervous system. The mechanisms involved in neuropathic pain are complex and involve both peripheral and central phenomena. Although numerous pharmacological agents are available for the treatment of neuropathic pain, definitive drug therapy has remained elusive. Recent drug discovery efforts have identified an original neurobiological approach to the pathophysiology of neuropathic pain. The development of innovative pharmacological strategies has led to the identification of new promising pharmacological targets, including glutamate antagonists, microglia inhibitors and, interestingly, endogenous ligands of cannabinoids and the transient receptor potential vanilloid type 1 (TRPV1). Endocannabinoids (ECs), endovanilloids and the enzymes that regulate their metabolism represent promising pharmacological targets for the development of a successful pain treatment. This review is an update of the relationship between cannabinoid receptors (CB1) and TRPV1 channels and their possible implications for neuropathic pain. The data are focused on endogenous spinal mechanisms of pain control by anandamide, and the current and emerging pharmacotherapeutic approaches that benefit from the pharmacological modulation of spinal EC and/or endovanilloid systems under chronic pain conditions will be discussed.     

Genomics: success or failure to deliver drug targets?

For decades, the entire pharmaceutical industry has focused on a limited number of drug targets. Owing to advances in molecular biology and genome technology at the beginning of the 1990s, discovery and isolation of a large number of genes from the human genome became feasible. This triggered a multi billion US dollars investment by both biotechnology and pharmaceutical companies to gain access to and patent as many potential drug targets as possible. Although the combined effort of publicly funded projects and private investments resulted in rapid identification of essentially all genes of the human genome, harnessing this information to enable drug discovery has turned out to be more challenging and time consuming than initially anticipated.     

天然活性成分作用靶点识别及确证方法的研究进展

药物靶标的发现和验证是新药研发的关键环节,对新药创制具有源头创新意义。天然产物是新药创制的重要来源,识别其作用靶点不仅为临床预防治疗提供可能新策略,也为进一步阐释中草药及其复方的作用特点及分子机制提供参考依据。随着生命科学和信息学的发展,药物靶点的识别及确证方法不断涌现,生物信息学、网络药理学、蛋白质组学、亲和色谱、药物亲和稳定性、芯片技术、基因敲除技术、RNA干扰等技术的广泛应用,越来越多的天然活性成分的靶点得以识别和验证。因此,本文对近五年来天然活性成分作用靶点识别及确证方法做一简要综述,以供参考。     

Integrated technology platform protein kinases for drug development in oncology

Protein kinases are among the most promising targets for drug discovery and development, mostly in oncology but also in other fields such as inflammation, Alzheimer's, and infectious diseases. The Integrated Technology Platform Protein Kinases was designed as a comprehensive tool for drug discovery in thefield of oncology. It combines modules for the identification and validation of novel target protein kinases, a unique panel of active recombinant protein kinases, high-throughput screening, selectivity profiling, cellular testing, and in vivo tumor models. Here we give an overview of the Integrated Technology Platform Protein Kinases as well as data that validate each module.     

The use of human tissue in drug discovery

Experiments conducted on human tissue samples are a key component of modern drug discovery programs and complement the use of animal tissue based assays in this process. Such studies can (i) enhance our understanding of disease pathophysiology, (ii) increase (or decrease) confidence that modulating the function of particular molecular targets will have therapeutic benefit (iii) allow comparison of the activities of different agents on particular mechanisms/processes and (iv) provide information on the potential safety risks associated with targets. All of this information is critical in identifying the targets that are most likely to deliver efficacious and safe medicines to address unmet clinical needs. With the introduction of new technologies, human tissue samples are also increasingly being incorporated into drug project screening cascades, including their use in high throughput assays. Improved access to human tissue would undoubtedly further extend the utility of this valuable resource in the drug discovery process.     

Target-based drug discovery,genetic diseases,and biologics

The last fifteen years have witnessed a major strategic shift in drug discovery away from an empiric approach based on incremental improvements of proven therapies, to a more theoretical, target-based approach. This arose as a consequence of three technical advances: (1) generation and interpretation of genome sequences, which facilitated identification and characterization of potential drug targets; (2) efficient production of candidate ligands for these putative targets through combinatorial chemistry or generation of monoclonal antibodies; and (3) high-throughput screening for rapid evaluation of interactions of these putative ligands with the selected targets. The basic idea underlying all three of these technologies is in keeping with Marshall Nirenberg’s dictum that science progresses best when there are simple assays capable of generating large data sets rapidly. Furthermore, practical implementation of target-based drug discovery was enabled directly by technologies that either were originated or nurtured by Marshall, his post-docs and fellows. Chief among these was the genetic code. Also important was adoption of clonal cell lines for pharmacological investigations, as well as the use of hybridomas to generate molecular probes that allowed physical purchase on signaling elements that had previously been only hypothetical constructs. Always the pure scientist, Marshall’s contributions nevertheless enabled fruitful applications in the pharmaceutical industry, several of them by his trainees. Both the successes and the shortcomings of target-based drug discovery are worthy of consideration, as are its implications for the choices of therapeutic goals and modalities by the pharmaceutical industry.     

Drug target identification from protein dynamics using quantitative pathway analysis

Dynamic proteomics promises to greatly facilitate identification of target proteins for drug molecules. Cohen et al. [Science, 2008, 322 (5907), 1511-1516] illustrated this potential, with the responses of 812 fluorescently tagged proteins to camptothecin administration monitored over 48 h. Directly from this data, one can restrict the list of candidate targets to 52 proteins. However, this approach has numerous limitations: equipment, labor (tagging and analyzing ≥1 colony/protein), and data analysis (aggregating individual cell data into population-relevant data sets). Furthermore, analytical success requires both explicit knowledge of drug target time-course evolution and, most importantly, monitoring of the target, itself. To address these issues, we developed a quantitative pathway analysis (qPA) technique, which employs well-annotated signaling pathways and elucidates putative drug targets and other molecules of interest. qPA, using more general assumptions and only 3 out of 144 available time points, identified the single known camptothecin target, TOPI, among only a handful of putative targets. Importantly, identification was possible without containing TOPI within the input data. These results demonstrate the potential of qPA in drug target discovery and highlight the importance of systems biology approaches for analysis of proteomics data.     

The use of peptides in Diogenesis: a novel approach to drug discovery and phenomics

The Diogenesis Process is an integrated drug discovery platform that allows target validation, partner identification, and the identification of small molecule drug candidates for protein:protein interactions. Diogenesis utilizes the well-established methods of peptide display, synthetic and recombinant peptide production, in vitro biochemical and cell-based testing to form a universal drug discovery engine with distinct advantages over competing protocols. The process creates a library of diverse peptides, and selects rare and unique binders that identify and simplify surface "hot spots" on protein targets through which target activity can be regulated. In many cases, these peptide "Surrogates" have the minimal sequence and structural information needed to induce a change in the biological activity of the target; in pharmacological terms, only after inducing agonism or antagonism. The use of Surrogates in hot spot identification also allows subdivision of rather large surface domains into smaller domains that alone, or in combination with another subdomain, offers sufficient territory for modification of target activity. These Surrogates, in turn, provide the necessary ligands to develop appropriate Site Directed Assays (SDAs) for each essential subdomain. The SDAs provide the screening mode for finding competitive small molecules by high throughput screening. The other arm of the Diogenesis system is an application in the new area of "Phenomics." This part of the discovery process is a form of phenotypic analysis of genomic information that has also been referred to as "functional" genomics. Phenomics, done via the Diogenesis system, uses peptide Surrogates as modifiers of the activity of, and identifiers of the partners of, gene products of known and unknown function. Actually, in many instances, the same Surrogate isolated for use in Phenomics will be used to create SDAs for discovery of small molecule drug candidates. In this simple fashion, the two applications of Diogenesis are integrated to provide savings in research time and money.     

Chemogenomics: drug discovery's panacea?

Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. This Opinion article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and opportunities that will impact drug discovery. Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. While historically the approach is based on efforts that systematically explore target gene families like kinases, today additional knowledge-based systematization principles are followed within early drug discovery projects which aim to biologically validate the targets and to identify starting points for chemical lead optimization. While the expectations of chemogenomics are very high, the reality of drug discovery is quite sobering with very high project attrition rates. This article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and potential opportunities that will impact drug discovery.     

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

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

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.