scholarly journals Perspective: The promises of a holistic view of proteins—impact on antibody engineering and drug discovery

2019 ◽  
Vol 39 (1) ◽  
Author(s):  
Ser-Xian Phua ◽  
Kwok-Fong Chan ◽  
Chinh Tran-To Su ◽  
Jun-Jie Poh ◽  
Samuel Ken-En Gan
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Antibody Engineering ◽  
Rational Drug Design ◽  
Biomedical Science ◽  
Great Promise ◽  
Holistic View ◽  
Rational Drug ◽  
Big Picture ◽  
Reductionist Approach

AbstractThe reductionist approach is prevalent in biomedical science. However, increasing evidence now shows that biological systems cannot be simply considered as the sum of its parts. With experimental, technological, and computational advances, we can now do more than view parts in isolation, thus we propose that an increasing holistic view (where a protein is investigated as much as a whole as possible) is now timely. To further advocate this, we review and discuss several studies and applications involving allostery, where distant protein regions can cross-talk to influence functionality. Therefore, we believe that an increasing big picture approach holds great promise, particularly in the areas of antibody engineering and drug discovery in rational drug design.

Antimalarial Drug Discovery: In Silico Structural Biology and Rational Drug Design

2009 ◽  
Vol 9 (3) ◽  
pp. 304-318 ◽  
Author(s):  
TAP de Beer ◽  
GA Wells ◽  
PB Burger ◽  
F. Joubert ◽  
E. Marechal ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Structural Biology ◽  
Antimalarial Drug ◽  
In Silico ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals CURRENT PROGRESS IN STRUCTURE-BASED RATIONAL DRUG DESIGN MARKS A NEW MINDSET IN DRUG DISCOVERY

2013 ◽  
Vol 5 (6) ◽  
pp. e201302011 ◽  
Author(s):  
Valère Lounnas ◽  
Tina Ritschel ◽  
Jan Kelder ◽  
Ross McGuire ◽  
Robert P. Bywater ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Rational Drug Design ◽  
Rational Drug

Halogen bonding for rational drug design and new drug discovery

2012 ◽  
Vol 7 (5) ◽  
pp. 375-383 ◽  
Author(s):  
Yunxiang Lu ◽  
Yingtao Liu ◽  
Zhijian Xu ◽  
Haiying Li ◽  
Honglai Liu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Halogen Bonding ◽  
Rational Drug Design ◽  
New Drug ◽  
Rational Drug

scholarly journals Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

2021 ◽  
Vol 71 (4) ◽  
pp. 225-256
Author(s):  
Milica Radan ◽  
Jelena Bošković ◽  
Vladimir Dobričić ◽  
Olivera Čudina ◽  
Katarina Nikolić
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Drug Design ◽  
Inflammatory Diseases ◽  
Rational Drug Design ◽  
Computer Aided Drug Design ◽  
Drug Discovery And Development ◽  
Rational Drug ◽  
Computer Aided ◽  
Anti Inflammatory

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

scholarly journals Nonadditivity in Public and Inhouse Data – Implications for Drug Design

2020 ◽  
Author(s):  
Dea Gogishvili ◽  
Eva Nittinger ◽  
Christian Margreitter ◽  
Christian Tyrchan
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
Measurement Errors ◽  
Rational Drug Design ◽  
Data Sets ◽  
Structure Activity ◽  
Rational Drug ◽  
Public Data ◽  
Sar Analysis

Abstract Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributions. These techniques are challenged by nonadditivity (NA) in protein-ligand binding where the change of two functional groups in one molecule results in much higher or lower activity than expected from the respective single changes. Identifying nonlinear cases and possible underlying explanations is crucial for a drug design project since it might influence which lead to follow. By systematically analyzing all AstraZeneca (AZ) inhouse compound data and publicly available ChEMBL25 bioactivity data, we show significant NA events in almost every second assay among the inhouse and once in every third assay in public data sets. Furthermore, 9.4% of all compounds of the AZ database and 5.1% from public sources display significant additivity shifts indicating important SAR features or fundamental measurement errors. Using NA data in combination with machine learning showed that nonadditive data is challenging to predict and even the addition of nonadditive data into training did not result in an increase in predictivity. Overall, NA analysis should be applied on a regular basis in many areas of computational chemistry and can further improve rational drug design.

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