Extreme Open Science: Companies Sharing Compounds without Restriction

Although the human genome provides the blueprint for life, most of the proteins it encodes remain poorly studied. This perspective describes how one group of scientists, in seeking new targets for drug discovery, used open science through unrestricted sharing of small molecules to shed light on dark matter of the genome. Starting initially with a single pharmaceutical company before expanding to multiple companies, a precedent was established for sharing published kinase inhibitors as chemical tools. The integration of open science and kinase chemogenomics has supported the study of many new potential drug targets by the scientific community.

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In depth analysis of kinase cross screening data to identify chemical starting points for inhibition of the nek family of kinases

10.1101/137968 ◽

2017 ◽

Author(s):

Carrow I. Wells ◽

Nirav R. Kapadia ◽

Rafael M. Couñago ◽

David H. Drewry

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Small Molecule ◽

Kinase Inhibitors ◽

Biological Functions ◽

Chemical Probes ◽

High Quality ◽

Depth Analysis ◽

Number Of Citations ◽

Shed Light

AbstractPotent, selective, and cell active small molecule kinase inhibitors are useful tools to help unravel the complexities of kinase signaling. As the biological functions of individual kinases become better understood, they can become targets of drug discovery efforts. The small molecules used to shed light on function can also then serve as chemical starting points in these drug discovery efforts. The Nek family of kinases has received very little attention, as judged by number of citations in PubMed, yet they appear to play many key roles and have been implicated in disease. Here we present our work to identify high quality chemical starting points that have emerged due to the increased incidence of broad kinome screening. We anticipate that this analysis will allow the community to progress towards the generation of chemical probes and eventually drugs that target members of the Nek family.

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Druggable Transient Pockets in Protein Kinases

Molecules ◽

10.3390/molecules26030651 ◽

2021 ◽

Vol 26 (3) ◽

pp. 651

Author(s):

Koji Umezawa ◽

Isao Kii

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Protein Kinases ◽

Binding Sites ◽

Kinase Inhibitors ◽

Screening Methods ◽

Research Attention ◽

Folding Process ◽

Chemical Screening ◽

Inhibitory Mechanisms

Drug discovery using small molecule inhibitors is reaching a stalemate due to low selectivity, adverse off-target effects and inevitable failures in clinical trials. Conventional chemical screening methods may miss potent small molecules because of their use of simple but outdated kits composed of recombinant enzyme proteins. Non-canonical inhibitors targeting a hidden pocket in a protein have received considerable research attention. Kii and colleagues identified an inhibitor targeting a transient pocket in the kinase DYRK1A during its folding process and termed it FINDY. FINDY exhibits a unique inhibitory profile; that is, FINDY does not inhibit the fully folded form of DYRK1A, indicating that the FINDY-binding pocket is hidden in the folded form. This intriguing pocket opens during the folding process and then closes upon completion of folding. In this review, we discuss previously established kinase inhibitors and their inhibitory mechanisms in comparison with FINDY. We also compare the inhibitory mechanisms with the growing concept of “cryptic inhibitor-binding sites.” These sites are buried on the inhibitor-unbound surface but become apparent when the inhibitor is bound. In addition, an alternative method based on cell-free protein synthesis of protein kinases may allow the discovery of small molecules that occupy these mysterious binding sites. Transitional folding intermediates would become alternative targets in drug discovery, enabling the efficient development of potent kinase inhibitors.

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Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

Briefings in Bioinformatics ◽

10.1093/bib/bby010 ◽

2018 ◽

Vol 20 (4) ◽

pp. 1465-1474 ◽

Cited By ~ 13

Author(s):

Ming Hao ◽

Stephen H Bryant ◽

Yanli Wang

Keyword(s):

Drug Discovery ◽

Human Genome ◽

High Throughput Screening ◽

Drug Target ◽

Drug Targets ◽

Drug Repositioning ◽

Research Field ◽

Data Driven ◽

Source Codes ◽

Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

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A Systematic Review of Recently Reported Marine Derived Natural Product Kinase Inhibitors

Marine Drugs ◽

10.3390/md17090493 ◽

2019 ◽

Vol 17 (9) ◽

pp. 493 ◽

Cited By ~ 11

Author(s):

Li ◽

Wang ◽

Zhang ◽

Sajeevan ◽

...

Keyword(s):

Small Molecules ◽

Natural Product ◽

Drug Targets ◽

Kinase Inhibitors ◽

Protein Kinase Inhibitors ◽

Disease States ◽

Clinical Drugs ◽

Kinase Inhibitory Activity ◽

New Compounds ◽

Further Development

Protein kinases are validated drug targets for a number of therapeutic areas, as kinase deregulation is known to play an essential role in many disease states. Many investigated protein kinase inhibitors are natural product small molecules or their derivatives. Many marine-derived natural products from various marine sources, such as bacteria and cyanobacteria, fungi, animals, algae, soft corals, sponges, etc. have been found to have potent kinase inhibitory activity, or desirable pharmacophores for further development. This review covers the new compounds reported from the beginning of 2014 through the middle of 2019 as having been isolated from marine organisms and having potential therapeutic applications due to kinase inhibitory and associated bioactivities. Moreover, some existing clinical drugs based on marine-derived natural product scaffolds are also discussed.

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Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search †

Molecules ◽

10.3390/molecules24122233 ◽

2019 ◽

Vol 24 (12) ◽

pp. 2233 ◽

Cited By ~ 11

Author(s):

Michele Montaruli ◽

Domenico Alberga ◽

Fulvio Ciriaco ◽

Daniela Trisciuzzi ◽

Anna Rita Tondo ◽

...

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Similarity Search ◽

Drug Targets ◽

Search Algorithm ◽

Confidence Score ◽

Database Management System ◽

Therapeutic Drug ◽

Protein Drug ◽

Early Protein

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

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A broad activity screen in support of a chemogenomic map for kinase signalling research and drug discovery

Biochemical Journal ◽

10.1042/bj20121418 ◽

2013 ◽

Vol 451 (2) ◽

pp. 313-328 ◽

Cited By ~ 80

Author(s):

Yinghong Gao ◽

Stephen P. Davies ◽

Martin Augustin ◽

Anna Woodward ◽

Umesh A. Patel ◽

...

Keyword(s):

Signal Transduction ◽

Drug Discovery ◽

Small Molecules ◽

Chemical Structure ◽

Rational Design ◽

Kinase Inhibitors ◽

Biochemical Assays ◽

Potential Applications ◽

Specific Inhibitors ◽

Target Effects

Despite the development of a number of efficacious kinase inhibitors, the strategies for rational design of these compounds have been limited by target promiscuity. In an effort to better understand the nature of kinase inhibition across the kinome, especially as it relates to off-target effects, we screened a well-defined collection of kinase inhibitors using biochemical assays for inhibitory activity against 234 active human kinases and kinase complexes, representing all branches of the kinome tree. For our study we employed 158 small molecules initially identified in the literature as potent and specific inhibitors of kinases important as therapeutic targets and/or signal transduction regulators. Hierarchical clustering of these benchmark kinase inhibitors on the basis of their kinome activity profiles illustrates how they relate to chemical structure similarities and provides new insights into inhibitor specificity and potential applications for probing new targets. Using this broad dataset, we provide a framework for assessing polypharmacology. We not only discover likely off-target inhibitor activities and recommend specific inhibitors for existing targets, but also identify potential new uses for known small molecules.

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Multiplexed screening of thousands of natural products for protein-ligand binding in native mass spectrometry

10.33774/chemrxiv-2021-sc39c-v2 ◽

2021 ◽

Author(s):

Giang Nguyen ◽

Jack Bennett ◽

Sherrie Liu ◽

Sarah Hancock ◽

Daniel Winter ◽

...

Keyword(s):

Mass Spectrometry ◽

Natural Products ◽

Drug Discovery ◽

Small Molecules ◽

Natural Product ◽

Drug Targets ◽

Structural Diversity ◽

Native Mass Spectrometry ◽

Protein Targets ◽

Rapid Measurement

The structural diversity of natural products offers unique opportunities for drug discovery, but challenges associated with their isolation and screening can hinder the identification of drug-like molecules from complex natural product extracts. Here we introduce a mass spectrometry-based approach that integrates untargeted metabolomics with multistage, high-resolution native mass spectrometry to rapidly identify natural products that bind to therapeutically relevant protein targets. By directly screening crude natural product extracts containing thousands of drug-like small molecules using a single, rapid measurement, novel natural product ligands of human drug targets could be identified without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

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Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

Current Drug Targets ◽

10.2174/1389450120666181105152439 ◽

2019 ◽

Vol 20 (5) ◽

pp. 522-539 ◽

Cited By ~ 1

Author(s):

Surovi Saikia ◽

Manobjyoti Bordoloi ◽

Rajeev Sarmah

Keyword(s):

Clinical Trials ◽

Drug Discovery ◽

Phase I ◽

Human Genome ◽

Phase Ii ◽

Drug Targets ◽

Complex Diseases ◽

Phase Iii ◽

Computational Techniques ◽

Receptor Complexes

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including “off-target” effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

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Multiplexed screening of thousands of natural products for protein-ligand binding in native mass spectrometry

10.33774/chemrxiv-2021-sc39c ◽

2021 ◽

Author(s):

Giang Nguyen ◽

Jack Bennett ◽

Sherrie Liu ◽

Sarah Hancock ◽

Daniel Winter ◽

...

Keyword(s):

Mass Spectrometry ◽

Natural Products ◽

Drug Discovery ◽

Small Molecules ◽

Natural Product ◽

Drug Targets ◽

Structural Diversity ◽

Native Mass Spectrometry ◽

Protein Targets ◽

Rapid Measurement

The structural diversity of natural products offers unique opportunities for drug discovery, but challenges associated with their isolation and screening can hinder the identification of drug-like molecules from complex natural product extracts. Here we introduce a mass spectrometry-based approach that integrates untargeted metabolomics with multistage, high-resolution native mass spectrometry to rapidly identify natural products that bind to therapeutically relevant protein targets. By directly screening crude natural product extracts containing thousands of drug-like small molecules using a single, rapid measurement, novel natural product ligands of human drug targets could be identified without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

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