scholarly journals Combinatorial chemistry. Facing the challenge of chemical genomics

2001 ◽  
Vol 73 (9) ◽  
pp. 1487-1498 ◽  
Author(s):  
Ferenc Darvas ◽  
Gyorgy Dorman ◽  
Laszlo Urge ◽  
Istvan Szabo ◽  
Zsolt Ronai ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Combinatorial Chemistry ◽  
High Throughput Screening ◽  
Early Stage ◽  
Combinatorial Libraries ◽  
Specific Marker ◽  
Chemical Genomics ◽  
High Quality ◽  
Sequential Approach ◽  
Gene Map

In the age of high-throughput screening and combinatorial chemistry, the focus of drug discovery is to replace the sequential approach with the most effective parallel approach. By the completion of the human gene-map, understanding and healing a disease require the integration of genomics, proteomics, and, very recently, metabolomics with early utilization of diverse small-molecule libraries to create a more powerful "total" drug discovery approach.In this post-genomic era, there is an enhanced demand for information-enriched combinatorial libraries which are high-quality, chemically and physiologically stable, diverse, and supported by measured and predicted data. Furthermore, specific marker libraries could be used for early functional profiling of the genome, proteome, and metabolome. In this new operating model, called "combinatorial chemical genomics", an optimal combination of the marker and high-quality libraries provides a novel synergy for the drug discovery process at a very early stage.

scholarly journals Deep Imputation on Large-Scale Drug Discovery Data

2021 ◽  
Author(s):  
Benedict Irwin ◽  
Thomas Whitehead ◽  
Scott Rowland ◽  
Samar Mahmoud ◽  
Gareth Conduit ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Large Scale ◽  
Early Stage ◽  
Quantitative Structure Activity Relationship ◽  
Biological Properties ◽  
Data Repository ◽  
Activity Data ◽  
Predicted Values

More accurate predictions of the biological properties of chemical compounds would guide the selection and design of new compounds in drug discovery and help to address the enormous cost and low success-rate of pharmaceutical R&D. However this domain presents a significant challenge for AI methods due to the sparsity of compound data and the noise inherent in results from biological experiments. In this paper, we demonstrate how data imputation using deep learning provides substantial improvements over quantitative structure-activity relationship (QSAR) machine learning models that are widely applied in drug discovery. We present the largest-to-date successful application of deep-learning imputation to datasets which are comparable in size to the corporate data repository of a pharmaceutical company (678,994 compounds by 1166 endpoints). We demonstrate this improvement for three areas of practical application linked to distinct use cases; i) target activity data compiled from a range of drug discovery projects, ii) a high value and heterogeneous dataset covering complex absorption, distribution, metabolism and elimination properties and, iii) high throughput screening data, testing the algorithm’s limits on early-stage noisy and very sparse data. Achieving median coefficients of determination, R, of 0.69, 0.36 and 0.43 respectively across these applications, the deep learning imputation method offers an unambiguous improvement over random forest QSAR methods, which achieve median R values of 0.28, 0.19 and 0.23 respectively. We also demonstrate that robust estimates of the uncertainties in the predicted values correlate strongly with the accuracies in prediction, enabling greater confidence in decision-making based on the imputed values.

scholarly journals The Effects of Combinatorial Chemistry and Technologies on Drug Discovery and Biotechnology – a Mini Review

2014 ◽  
Vol 13 (2) ◽  
pp. 87-108 ◽  
Author(s):  
Pierfausto Seneci ◽  
Giorgio Fassina ◽  
Vladimir Frecer ◽  
Stanislav Miertus
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Expert Opinion ◽  
High Throughput ◽  
Combinatorial Chemistry ◽  
Rational Design ◽  
Combinatorial Libraries ◽  
Computer Assisted ◽  
Future Trends ◽  
The Impact

Abstract The review will focus on the aspects of combinatorial chemistry and technologies that are more relevant in the modern pharmaceutical process. An historical, critical introduction is followed by three chapters, dealing with the use of combinatorial chemistry/high throughput synthesis in medicinal chemistry; the rational design of combinatorial libraries using computer-assisted combinatorial drug design; and the use of combinatorial technologies in biotechnology. The impact of “combinatorial thinking” in drug discovery in general, and in the examples reported in details, is critically discussed. Finally, an expert opinion on current and future trends in combinatorial chemistry and combinatorial technologies is provided.

Drug Discovery

2019 ◽  
pp. 1-46
Author(s):  
S. Lakshmana Prabu
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Combinatorial Chemistry ◽  
High Throughput Screening ◽  
Development Stage ◽  
Synthetic Chemistry ◽  
Success Rates ◽  
Herbal Plants ◽  
Pharmaceutical Industries ◽  
The 1960S

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

The maturation of DNA encoded libraries: opportunities for new users

2020 ◽  
Author(s):  
Daniel Conole ◽  
James H Hunter ◽  
Michael J Waring
Keyword(s):  
Data Analysis ◽  
Cost Effectiveness ◽  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
New Technology ◽  
Combinatorial Libraries ◽  
Library Design ◽  
First Time ◽  
Screening Technology

DNA-encoded combinatorial libraries (DECLs) represent an exciting new technology for high-throughput screening, significantly increasing its capacity and cost–effectiveness. Historically, DECLs have been the domain of specialized academic groups and industry; however, there has recently been a shift toward more drug discovery academic centers and institutes adopting this technology. Key to this development has been the simplification, characterization and standardization of various DECL subprotocols, such as library design, affinity screening and data analysis of hits. This review examines the feasibility of implementing DECL screening technology as a first-time user, particularly in academia, exploring the some important considerations for this, and outlines some applications of the technology that academia could contribute to the field.

scholarly journals Natural Products in High Throughput Screening: Automated High-Quality Sample Preparation

1999 ◽  
Vol 4 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Ingrid Schmid ◽  
Isabel Sattler ◽  
Susanne Grabley ◽  
Ralf Thiericke
Keyword(s):  
Drug Discovery ◽  
Sample Preparation ◽  
High Throughput ◽  
High Throughput Screening ◽  
Solid Phase ◽  
Structural Diversity ◽  
High Quality ◽  
Synthetic Compound ◽  
Natural Origin ◽  
Compound Libraries

At present, compound libraries from combinatorial chemistry are the major source for high throughput screening (HTS) programs in drug discovery. On the other hand, nature has been proven to be an outstanding source for new and innovative drugs. Secondary metabolites from plants, animals, and microorganisms show a striking structural diversity that supplements chemically synthesized compounds or libraries in drug discovery programs. Unfortunately, extracts from natural sources are usually complex mixtures of compounds, often generated in time-consuming and, for the most part, manual processes. Because quality and quantity of the provided samples play a pivotal role in the success of HTS programs, this poses serious problems. In order to make samples of natural origin competitive with synthetic compound libraries, we devised a novel, automated sample preparation procedure based on solid-phase extraction (SPE). By making use of modified Zymark (Hopkinton, MA) RapidTrace® SPE workstations, we developed an easy-to-handle and effective fractionation method that generates high-quality samples from natural origin, fulfilling the requirements for an integration in high throughput drug discovery programs.

scholarly journals Managing laboratory automation: integration and informatics in drug discovery

2000 ◽  
Vol 22 (6) ◽  
pp. 169-170 ◽  
Author(s):  
Charles J. Manly
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Combinatorial Chemistry ◽  
High Throughput Screening ◽  
Laboratory Automation ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Modern Drug

Drug discovery today requires the focused use of laboratory automation and other resources in combinatorial chemistry and high-throughput screening (HTS). The ultimate value of both combinatorial chemistry and HTS technologies and the lasting impact they will have on the drug discovery process is a chapter that remains to be written. Central to their success and impact is how well they are integrated with each other and with the rest of the drug discovery processes-informatics is key to this success. This presentation focuses on informatics and the integration of the disciplines of combinatorial chemistry and HTS in modern drug discovery. Examples from experiences at Neurogen from the last five years are described.

Drug Discovery (Lead Identification and High Throughput Screening)

2021 ◽  
pp. 46-50
Author(s):  
Ravi Kumar
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Clinical Development ◽  
High Quality ◽  
New Drug ◽  
Lead Identification ◽  
Pharmaceutical Industries

In this review we will discuss about the Lead identification, the lead identification is mostly used for the discovery of successful clinical development compound, and it is an essential site for drug discovery. Various important factors that required for discovery a quality leads, such as- Physicochemical, ADME, Biological and PK parameters. These all parameters are required for the identification of high-quality leads. The Combinational chemistry is mostly used for the generation of many compounds in only one process from a mixture. The high throughput screening is suitable for new drug in pharmaceutical industries and it’s mostly used from last two decades.

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