From Computers to Bedside: Computational Chemistry Contributing to
            <scp>FDA</scp>
            Approval

Athanasiou, Christina; Cournia, Zoe

doi:10.1002/9783527806836.ch7

Cited by 7 publications

(4 citation statements)

References 194 publications

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“…They are used to gain insight into not only how ligands bind to target proteins, but also the pathways of interaction and to account for target flexibility. The most well-known examples of how MD simulations have contributed to the development FDA-approved drugs are Raltegravir, a HIV integrase inhibitor [ 64 , 65 ] and Zanamivir, a neuraminidase inhibitor against influenza A and B virus [ 66 ]. More details are fully described in references [ 55 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ].…”

Section: Methodsmentioning

confidence: 99%

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

Jawad

Adhikari

Cheng

et al. 2022

IJMS

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A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a −1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

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Section: Methodsmentioning

confidence: 99%

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

Jawad

Adhikari

Cheng

et al. 2022

IJMS

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“…Similarly, a number of FDA-approved small molecule kinase inhibitors have been discovered with the aid of computational techniques, primarily structure-based drug design methods, as shown in Table 1, as a result of advancements in molecular graphics that permit visualization of the crystal structure to extract the details of molecular interactions between protein and ligands. [130] Ligand-based drug design methods offer the same potential to contribute in the drug discovery and development process as structure-based methods. However, we were unable to identify any examples of authorized kinase inhibitors that were found using machine learning techniques.…”

Section: From Computers To Marketmentioning

confidence: 99%

Development of Kinase‐Centric Drugs: A Computational Perspective

Gajulapalli¹

2023

ChemMedChem

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Kinases are involved in signal transduction, physiological responses, and diseases like cancer, neurological disorders, and autoimmune disorders, making them popular drug targets. Since Gleevec was approved to treat chronic myelogenous leukemia, several FDA‐approved small‐molecule drugs have been created. Kinases were initially "undruggable". Several FDA‐approved small‐molecule drugs have been released recently. Few medications target allosteric sites, but most target ATP‐binding sites. The catalytic domain has high structural and sequence conservation among family kinases. ATP‐binding inhibitors can bind off‐target. Kinases and inhibitors often have intricate connections due to similar sequences and structures. Understanding target selectivity for kinase inhibitors is crucial for drug design and development. Several experimental methodologies profile small molecule kinase selectivities to produce novel inhibitors with the appropriate selectivity. Experimental methods are costly, time‐consuming, and kinase limited. The researchers applied computational methods to overcome these barriers in treatment design and development. Over the last few decades, many computational methods have been developed to supplement experimental data or predict kinase inhibitor efficacy and selectivity. This paper discusses current theoretical/computational breakthroughs in kinase inhibitor design with the necessary selectivity and optimization.

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“…The most frequently used methods in SBDD, that is, MD simulations, molecular docking, and structure-based VS, are applied to evaluate binding affinity and ligand–target interactions and explore conformational changes in the target. Using SBDD, some approved drugs, such as Imatinib (an abltyrosine kinase inhibitor) [ 154 ], Indinavir (Crixivan, the inhibitor of HIV-1 protease) [ 155 ], Nilotinib (Tasigna, a selective tyrosine kinase receptor inhibitor used in the treatment of chronic myelogenous leukemia) [ 156 ], and Lifitegrast (the LFA-1 antagonist that blocks binding of ICAM-1 to LFA-1) [ 157 ], were discovered. SBDD mainly includes target preparation, binding site identification, compound library preparation, molecular docking and scoring, and MD simulations ( Figure 2 ).…”

Section: Computer-aided Drug Designmentioning

confidence: 99%

Application of Computational Biology and Artificial Intelligence in Drug Design

Zhang

Luo

et al. 2022

IJMS

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Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

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From Computers to Bedside: Computational Chemistry Contributing to FDA Approval

Cited by 7 publications

References 194 publications

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

Development of Kinase‐Centric Drugs: A Computational Perspective

Application of Computational Biology and Artificial Intelligence in Drug Design

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