掲載種別：研究論文（学術雑誌）  

DOI： 10.1021/acs.jcim.4c00759

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jgsce.2024.205417

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1080/27660400.2023.2210251

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1021/acsomega.3c05430

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1021/acsomega.3c01314

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/csce60160.2023.00352

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1080/27660400.2022.2066489

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

Computational science has attracted significant attention as a means of reducing research and development costs in the field of drug discovery. An example of compound discovery is to propose numerous compounds using molecular generation models, and then filter them by determining whether they can be synthesized or not by a computer before proceeding to wet experiments. Filtering can be done by retrosynthesis or by scoring. An example of existing scoring methods is RAs-core, which predicts the retrosynthetic accessibility by machine learning. Therefore, this study discusses the practicality of the RAscore model and its associated problems. It also proposes a method for building a model that can make more accurate predictions based on hypotheses about the causes of the problem. In addition to the ChEMBL data used to train the original model, we created three models using data created with the ChemTS molecular generation model, and selected the model with the best evaluation results. The models were evaluated by comparing scores using existing RAscore models as a baseline, and one model of the three models achieved a better AUC (area under the curve) and binary accuracy. The models, datasets, and source code are available at https://github.com/sekijima-lab/retrosynthesizability_prediction_models.

DOI： 10.1109/bibe55377.2022.00052

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier {BV}  

DOI： 10.1016/j.ecoenv.2022.113971

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掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society ({ACS})  

DOI： 10.1021/acs.jcim.1c01087

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jmgm.2021.108044

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead Monoclonal antibody (mAb) drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called Aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed Molecular Dynamics (MD) simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multi-copy peptide simulation systems of 'VLVIY' and 'KLVIY' were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide 'VLVIY' formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Further, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val➔Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability. This article is protected by copyright. All rights reserved.

DOI： 10.1002/prot.26230

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

DOI： 10.1016/j.parint.2021.102366

PubMed

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掲載種別：研究論文（学術雑誌）  

DOI： 10.26434/chemrxiv.14450313.v1

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.5143082

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DOI： 10.26434/chemrxiv.12009636.v1

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

<title>Abstract</title>Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using <italic>in silico</italic> methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

DOI： 10.1038/s41598-019-55069-y

PubMed

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その他リンク： http://www.nature.com/articles/s41598-019-55069-y

記述言語：英語   掲載種別：研究論文（学術雑誌）  

Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding affinity, target selectivity, physicochemical properties, and toxicity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods.

DOI： 10.2197/ipsjtbio.11.41

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1142/S0219720018400164

PubMed

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

Abstract

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

DOI： 10.1038/s41598-017-10275-4

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その他リンク： https://www.nature.com/articles/s41598-017-10275-4

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41598-017-06411-9

Web of Science

PubMed

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

Abstract

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

DOI： 10.1038/srep17209

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その他リンク： https://www.nature.com/articles/srep17209.pdf

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0125829

Web of Science

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