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

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2233 ◽  
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.

scholarly journals SELDI ProteinChip®Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications

2003 ◽  
Vol 2003 (4) ◽  
pp. 237-241 ◽  
Author(s):  
Guru Reddy ◽  
Enrique A. Dalmasso
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Treatment Options ◽  
Drug Effects ◽  
Proteome Analysis ◽  
Predictive Medicine ◽  
Protein Drug ◽  
Proteinchip Array ◽  
Drug Candidates ◽  
Array Technology

Predictive medicine, utilizing the ProteinChip®Array technology, will develop through the implementation of novel biomarkers and multimarker patterns for detecting disease, determining patient prognosis, monitoring drug effects such as efficacy or toxicity, and for defining treatment options. These biomarkers may also serve as novel protein drug candidates or protein drug targets. In addition, the technology can be used for discovering small molecule drugs or for defining their mode of action utilizing protein-based assays. In this review, we describe the following applications of the ProteinChip Array technology: (1) discovery and identification of novel inhibitors of HIV-1 replication, (2) serum and tissue proteome analysis for the discovery and development of novel multimarker clinical assays for prostate, breast, ovarian, and other cancers, and (3) biomarker and drug discovery applications for neurological disorders.

scholarly journals A Review of Deep Learning Methods for Antibodies

Antibodies ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 12 ◽  
Author(s):  
Jordan Graves ◽  
Jacob Byerly ◽  
Eduardo Priego ◽  
Naren Makkapati ◽  
S. Vince Parish ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Drug Discovery ◽  
Small Molecules ◽  
Language Processing ◽  
Protein Design ◽  
Therapeutic Drug ◽  
Major Goal ◽  
Antibody Design ◽  
Antibody Drug

Driven by its successes across domains such as computer vision and natural language processing, deep learning has recently entered the field of biology by aiding in cellular image classification, finding genomic connections, and advancing drug discovery. In drug discovery and protein engineering, a major goal is to design a molecule that will perform a useful function as a therapeutic drug. Typically, the focus has been on small molecules, but new approaches have been developed to apply these same principles of deep learning to biologics, such as antibodies. Here we give a brief background of deep learning as it applies to antibody drug development, and an in-depth explanation of several deep learning algorithms that have been proposed to solve aspects of both protein design in general, and antibody design in particular.

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

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.

scholarly journals Natural product drug discovery in the artificial intelligence era

2022 ◽  
Author(s):  
Fernanda I Saldivar-Gonzalez ◽  
Victor Daniel Aldas-Bulos ◽  
José Luis Medina-Franco ◽  
Fabien Plisson
Keyword(s):  
Artificial Intelligence ◽  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Drug Targets ◽  
Structural Diversity ◽  
Protein Drug ◽  
Privileged Structures

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even...

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

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.

scholarly journals New Approaches to Difficult Drug Targets: The Phosphatase Story

2017 ◽  
Vol 22 (9) ◽  
pp. 1071-1083 ◽  
Author(s):  
John S. Lazo ◽  
Kelley E. McQueeney ◽  
Elizabeth R. Sharlow
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Protein Tyrosine Phosphatase ◽  
Drug Targets ◽  
Tyrosine Phosphatase ◽  
Molecular Targets ◽  
Protein Tyrosine Phosphatases ◽  
Protein Tyrosine ◽  
Further Development ◽  
Enzyme Class

The drug discovery landscape is littered with promising therapeutic targets that have been abandoned because of insufficient validation, historical screening failures, and inferior chemotypes. Molecular targets once labeled as “undruggable” or “intractable” are now being more carefully interrogated, and while they remain challenging, many target classes are appearing more approachable. Protein tyrosine phosphatases represent an excellent example of a category of molecular targets that have emerged as druggable, with several small molecules and antibodies recently becoming available for further development. In this review, we examine some of the diseases that are associated with protein tyrosine phosphatase dysfunction and use some prototype contemporary strategies to illustrate approaches that are being used to identify small molecules targeting this enzyme class.

scholarly journals A Perspective on Extreme Open Science: Companies Sharing Compounds without Restriction

2019 ◽  
Vol 24 (5) ◽  
pp. 505-514 ◽  
Author(s):  
David H. Drewry ◽  
Carrow I. Wells ◽  
William J. Zuercher ◽  
Timothy M. Willson
Keyword(s):  
Dark Matter ◽  
Drug Discovery ◽  
Small Molecules ◽  
Drug Targets ◽  
Scientific Community ◽  
Kinase Inhibitors ◽  
Open Science ◽  
Potential Drug ◽  
Potential Drug Targets ◽  
Shed Light

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.

Natural Thiazoline-Based Cyclodepsipeptides from Marine Cyanobacteria: Chemistry, Bioefficiency and Clinical Aspects

2021 ◽  
Vol 28 ◽  
Author(s):  
Rajiv Dahiya ◽  
Sunita Dahiya ◽  
Neeraj Kumar Fuloria ◽  
Satish Jankie ◽  
Alka Agarwal ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Drug Targets ◽  
Cyclic Peptides ◽  
Chemical Space ◽  
Clinical Success ◽  
Clinical Status ◽  
Structural Features ◽  
Clinical Aspects ◽  
Medical Needs

Background: Peptides and peptide-based therapeutics are biomolecules that demarcate a significant chemical space to bridge small molecules with biological therapeutics such as antibodies, recombinant proteins, and protein domains. Introduction: Cyclooligopeptides and depsipeptides, particularly cyanobacteria-derived thiazoline-based cyclopolypeptides (CTBCs), exhibit a wide array of pharmacological activities due to their unique structural features and interesting bioactions, which furnish them as promising leads for drug discovery. Method: In the present study, we comprehensively review the natural sources, distinguishing chemistries, and pertinent bioprofiles of CTBCs. We analyze their structural peculiarities counting the mode of actions for biological portrayals which render CTBCs as indispensable sources for the emergence of prospective peptide-based therapeutics. In this milieu, metal organic frameworks and their biomedical applications are also briefly discussed. To boot, the challenges, approaches, and clinical status of peptide-based therapeutics are conferred. Result: Based on these analyses, CTBCs can be appraised as ideal drug targets that have always remained a challenge for traditional small molecules, like those involved in protein-protein interactions or to be developed as potential cancer-targeting nanomaterials. Cyclization-induced reduced conformational freedom of these cyclooligopeptides contribute to improved metabolic stability and binding affinity to their molecular targets. Clinical success of several cyclic peptides provokes the large library-screening and synthesis of natural product-like cyclic peptides to address the unmet medical needs. Conclusion: CTBCs can be considered as the most promising lead compounds for drug discovery. Adopting the amalgamation of advanced biological and biopharmaceutical strategies might endure these cyclopeptides to be prospective biomolecules for futuristic therapeutic applications in the coming times.

scholarly journals Extreme Open Science: Companies Sharing Compounds without Restriction

2018 ◽  
Author(s):  
David Drewry ◽  
Carrow Wells ◽  
William J Zuercher ◽  
Timothy Mark Willson
Keyword(s):  
Dark Matter ◽  
Drug Discovery ◽  
Small Molecules ◽  
Human Genome ◽  
Pharmaceutical Company ◽  
Drug Targets ◽  
Kinase Inhibitors ◽  
Open Science ◽  
New Targets ◽  
Shed Light

Although the human genome provides the blueprint for life, most of the proteins it encodes remain poorly studied. We describe 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. As a result, new drug targets were identified and the science of kinase chemogenomics was established.

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