Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

Chiba, Shuntaro; Ikeda, Kazuyoshi; Ishida, Takashi; Gromiha, M. Michael; Taguchi, Y-h.; Iwadate, Mitsuo; Umeyama, Hideaki; Hsin, Kun‐Yi; Kitano, Hiroaki; Yamamoto, Kazuki; Sugaya, Nobuyoshi; Kato, Koya; Okuno, Tatsuya; Chikenji, George; Mochizuki, Masahiro; Yasuo, Nobuaki; Yoshino, Ryunosuke; Yanagisawa, Keisuke; Ban, Tao; Teramoto, Reiji; Thangakani, A. Mary; Velmurugan, D.; Prathipati, Philip; Ito, Junichi; Tsuchiya, Yuko; Mizuguchi, Kenji; Honma, Teruki; Hirokawa, Takatsugu; Akiyama, Yutaka; Sekijima, Masakazu

doi:10.1038/srep17209

Cited by 38 publications

(33 citation statements)

References 57 publications

(61 reference statements)

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“…Computational methods are commonly used for structure-based drug discovery (SBDD) and ligandbased drug discovery (LBDD) [14][15][16][17][18][19] . LBDD is a technique for searching and designing new drugs based on experimental information and structural information of known compounds 20,21 .…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Yoshino¹,

Yasuo²,

Sekijima

2020

Preprint

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The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against <a>SARS-CoV </a>Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.

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Section: Introductionmentioning

confidence: 99%

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Yoshino¹,

Yasuo²,

Sekijima

2020

Preprint

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“…Computational methods have now become indispensable part of drug design process, supporting its every stage, from proposing new drug candidates, via optimization of their activity, to tuning their physicochemical and pharmacokinetic properties and minimizing adverse effects (computer-aided drug design, CADD) [1][2][3][4][5][6][7][8]. Great desire for new medications for various diseases is an impulse for conduction of experiments in the field, which causes the exponential growth of the amount of pharmaceutical-related data that can then be used for modeling of compounds bioactivity and properties.…”

Section: Introductionmentioning

confidence: 99%

How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

2020

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A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able to evaluate in relatively short time enormous amounts of data. However, the quality of machine-learning-based prediction depends on the data supplied for model training. In this study, we used deep neural networks for the task of compound activity prediction and developed dropout-based approaches for estimating prediction uncertainty. Several types of analyses were performed: the relationships between the prediction error, similarity to the training set, prediction uncertainty, number and standard deviation of activity values were examined. It was tested whether incorporation of information about prediction uncertainty influences compounds ranking based on predicted activity and prediction uncertainty was used to search for the potential errors in the ChEMBL database. The obtained outcome indicates that incorporation of information about uncertainty of compound activity prediction can be of great help during virtual screening experiments.

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“…4 CADD is being utilized to expedite and facilitate hit identi¯cation and hit-to-lead selection; optimize the absorption, distribution, metabolism, excretion and toxicity pro¯le; and avoid safety issues. [5][6][7][8] In drug discovery, the theoretical number of compounds in chemical space is estimated to be as much as 10 60 . 9 By contrast, compound libraries possessed by pharmaceutical companies enumerate only a few million substances, leading to the serious problem of an in-su±cient search range.…”

Section: Introductionmentioning

confidence: 99%

Compound property enhancement by virtual compound synthesis

Arai

Yoshikawa

Yasuo

et al. 2018

J. Bioinform. Comput. Biol.

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During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

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Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

Cited by 38 publications

References 57 publications

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

Compound property enhancement by virtual compound synthesis

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