Mass Spectrometry in Drug Discovery Edited by David T. Rossi (Pfizer Global Research and Development) and Michael W. Sinz (Bristol-Myers Squibb, Wallingford, CT). Marcel Dekker, Inc.:  New York and Basel. 2002. xi + 420 pp. $165.00. ISBN 0-8247-0607-2.

2002 ◽  
Vol 124 (41) ◽  
pp. 12391-12392
Author(s):  
Steven A. Hofstadler
Keyword(s):  
Mass Spectrometry ◽  
New York ◽  
Drug Discovery ◽  
Research And Development ◽  
Global Research

A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery

2020 ◽  
Vol 25 (9) ◽  
pp. 963-976
Author(s):  
Richard J. A. Goodwin ◽  
Zoltan Takats ◽  
Josephine Bunch
Keyword(s):  
Mass Spectrometry ◽  
Drug Discovery ◽  
Research And Development ◽  
Unmet Need ◽  
Pharmaceutical Research ◽  
Vital Role ◽  
Therapeutic Modalities ◽  
Concise Review ◽  
Program Attrition ◽  
Endogenous Metabolites

During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.

High-Throughput Affinity Selection Mass Spectrometry Using SAMDI-MS to Identify Small-Molecule Binders of the Human Rhinovirus 3C Protease

2021 ◽  
pp. 247255522110232
Author(s):  
Michael D. Scholle ◽  
Doug McLaughlin ◽  
Zachary A. Gurard-Levin
Keyword(s):  
Mass Spectrometry ◽  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Human Rhinovirus ◽  
Binding Potential ◽  
Affinity Selection ◽  
Response Curves ◽  
Desorption Ionization ◽  
Self Assembled

Affinity selection mass spectrometry (ASMS) has emerged as a powerful high-throughput screening tool used in drug discovery to identify novel ligands against therapeutic targets. This report describes the first high-throughput screen using a novel self-assembled monolayer desorption ionization (SAMDI)–ASMS methodology to reveal ligands for the human rhinovirus 3C (HRV3C) protease. The approach combines self-assembled monolayers of alkanethiolates on gold with matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry (MS), a technique termed SAMDI-ASMS. The primary screen of more than 100,000 compounds in pools of 8 compounds per well was completed in less than 8 h, and informs on the binding potential and selectivity of each compound. Initial hits were confirmed in follow-up SAMDI-ASMS experiments in single-concentration and dose–response curves. The ligands identified by SAMDI-ASMS were further validated using differential scanning fluorimetry (DSF) and in functional protease assays against HRV3C and the related SARS-CoV-2 3CLpro enzyme. SAMDI-ASMS offers key benefits for drug discovery over traditional ASMS approaches, including the high-throughput workflow and readout, minimizing compound misbehavior by using smaller compound pools, and up to a 50-fold reduction in reagent consumption. The flexibility of this novel technology opens avenues for high-throughput ASMS assays of any target, thereby accelerating drug discovery for diverse diseases.

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

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