Targeting NAMPT for Therapeutic Intervention in Cancer and Inflammation: Structure-Based Drug Design and Biological Screening

Casein kinase 1 (CK1) is an extensively expressed serine/threonine kinase family, with six highly conserved isoforms of human CK1. Due to its involvement in many biological processes, CK1 is a promising target for several pathological states, including circadian sleep disorder, neurodegenerative diseases, cancer and inflammation. However, due to the structural similarities between the six CK1 members, the design of CK1 inhibitors is intricate. So far, no effective CK1 inhibitors are reported to reach clinical trials; thus, approaches to obtaining both selective and effective CK1 inhibitors are in great demand. Here we analyze several CK1 inhibitors that provide successful experience for structure-based drug design and rational structure modification, which could provide references for further drug design.

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Antibody- and Peptide Structure-Based Drug Design

PsycEXTRA Dataset ◽

10.1037/e495942006-004 ◽

1993 ◽

Author(s):

Manfred E. Wolff ◽

Keyword(s):

Drug Design ◽

Peptide Structure ◽

Structure Based Drug Design

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Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

10.26434/chemrxiv.9866912 ◽

2019 ◽

Author(s):

Mohammad Rezaei ◽

Yanjun Li ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Binding Affinity ◽

Benchmark Dataset ◽

Rational Drug Design ◽

Learning Models ◽

Structure Based Drug Design ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Rational Drug

Introduction: The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results. Objectives: The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters. Methods: The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models. Results: The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model. Conclusions: The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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Faculty Opinions recommendation of Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK).

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.12934957.14822055 ◽

2012 ◽

Author(s):

John Proudfoot

Keyword(s):

Drug Design ◽

Anaplastic Lymphoma Kinase ◽

Dual Inhibitor ◽

Structure Based Drug Design ◽

Anaplastic Lymphoma ◽

Mesenchymal Epithelial Transition Factor ◽

Factor C ◽

Mesenchymal Epithelial Transition

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Faculty Opinions recommendation of Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK).

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.12934957.14323054 ◽

2011 ◽

Author(s):

John Lowe

Keyword(s):

Drug Design ◽

Anaplastic Lymphoma Kinase ◽

Dual Inhibitor ◽

Structure Based Drug Design ◽

Anaplastic Lymphoma ◽

Mesenchymal Epithelial Transition Factor ◽

Factor C ◽

Mesenchymal Epithelial Transition

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Faculty Opinions recommendation of Free Ligand 1D NMR Conformational Signatures to Enhance Structure Based Drug Design of a Mcl-1 inhibitor (AZD5991) and other Synthetic Macrocycles.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.736292329.793563510 ◽

2019 ◽

Author(s):

John Lowe

Keyword(s):

Drug Design ◽

Free Ligand ◽

Structure Based Drug Design

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Preface ( Hot Topics: ADMET / Tox Informatics: Adding Innovation to Structure-Based Drug Design Guest Editor: Roberta Bursi ).

Current Topics in Medicinal Chemistry ◽

10.2174/1568026033451943 ◽

2003 ◽

Vol 3 (11) ◽

pp. 1-2

Author(s):

R. Bursi

Keyword(s):

Drug Design ◽

Guest Editor ◽

Structure Based Drug Design

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Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/156802662019200701164759 ◽

2020 ◽

Vol 20 (19) ◽

pp. 1651-1660

Author(s):

Anuraj Nayarisseri

Keyword(s):

Drug Discovery ◽

Drug Design ◽

Binding Affinity ◽

Chemical Biology ◽

De Novo ◽

Structure Based Drug Design ◽

Computational Approaches ◽

Drug Designing ◽

Traditional Drug ◽

Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

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