A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Chiba, Shuntaro; Ohue, Masahito; Gryniukova, Anastasiia; Borysko, Petro; Zozulya, Sergey; Yasuo, Nobuaki; Yoshino, Ryunosuke; Ikeda, Kazuyoshi; Shin, Woong‐Hee; Iwadate, Mitsuo; Umeyama, Hideaki; Ichikawa, Takaaki; Teramoto, Reiji; Hsin, Kun Yi; Gupta, Vipul; Kitano, Hiroaki; Sakamoto, Mika; Higuchi, Akiko; Miura, Nobuaki; Yura, Kei; Mochizuki, Masahiro; Thangakani, A. Mary; Velmurugan, D.; Gromiha, M. Michael; Nakane, Itsuo; Uchida, Nanako; Hakariya, Hayase; Tan, Modong; Nakamura, Hironori; Suzuki, Shogo D.; Ito, Tetsuhide; Kawatani, Masahiro; Kudoh, Kentaroh; Takashina, Sakurako; Yamamoto, Kazuki; Moriwaki, Yoshitaka; Oda, Kazuhiro; Kobayashi, Daisuke; Okuno, Tatsuya; Minami, Shintaro; Chikenji, George; Prathipati, Philip; Nagao, Chioko; Mohsen, Attayeb; Ito, Mari; Mizuguchi, Kenji; Honma, Teruki; Ishida, Takashi; Hirokawa, Takatsugu; Akiyama, Yutaka; Sekijima, Masakazu

doi:10.1038/s41598-019-55069-y

Cited by 23 publications

(31 citation statements)

References 68 publications

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“…Our method achieves a hit rate of 2.40% if compounds with measured K D values are taken into account, and 5.60% if weak binders are considered. This translates to an 11-and 25-fold improvement, respectively, over other virtual screening techniques [31].…”

Section: Discussionmentioning

confidence: 95%

“…If only compounds with measured K D values are taken into account, the hit rate is 2.40%. A benchmark study by Chiba et al [31], where virtual screening predictions of 16 scientific groups were combined and assessed in vitro, achieved a hit rate of 0.22% for the NAD-dependent deacetylase sirtuin-1. Based on these findings, with the results presented in this case study, the PharmAI DiscoveryEngine shows an 11-to 25-fold improvement over existing technologies.…”

Section: Hit Ratementioning

confidence: 99%

“…Based on these findings, with the results presented in this case study, the PharmAI DiscoveryEngine shows an 11-to 25-fold improvement over existing technologies. Simultaneously, the number of compounds needed to be screened was 25 times lower (125 vs. 3192), while still achieving the same number of seven hits [31]. In a traditional HTS setup with a hit rate of around 0.02% [32], in vitro testing of 35,000 compounds would have been necessary to achieve a comparable number of hits.…”

Section: Hit Ratementioning

confidence: 99%

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Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors within silicoDriven Library Prioritization and MicroScale Thermophoresis

Kaiser¹,

Plach²,

Schubert³

et al. 2020

Preprint

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Accelerated development of lead structures is of high interest to the pharmaceutical industry in order to decrease development times and costs. We showcase how an intelligent combination of AI-based drug screening with stateof-the-art biophysics drives the rapid identification of novel inhibitor structures with high chemical diversity for cGMP-dependent 3',5'-cyclic phosphodiesterase (PDE2). The starting point was an off-the-shelve chemical library of two million drug-like compounds. In a single in silico reduction step, we short-listed 125 compounds -the focused library -as potential binders to PDE2 and tested their binding behavior in vitro using MicroScale Thermophoresis (MST). Of this focused library, seven compounds indicated binding to PDE2, translating to a hit rate of 6%. Three of these compounds have affinities in the lower micromolar range. The compound with the highest affinity showed a K D of 10 µM and is thus an excellent starting point for further medicinal chemistry optimization. The results show how innovative and structure-driven in silico approaches and biophysics can be used to accelerate drug discovery and to obtain new molecular scaffolds at a fraction of the costs and time -compared with standard high-throughput screening.

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Section: Discussionmentioning

confidence: 95%

Section: Hit Ratementioning

confidence: 99%

Section: Hit Ratementioning

confidence: 99%

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Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors within silicoDriven Library Prioritization and MicroScale Thermophoresis

Kaiser¹,

Plach²,

Schubert³

et al. 2020

Preprint

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show abstract

“…4 In virtual screening, compounds that have a high potential to bind to a target protein are ranked in order from a database of thousands to millions of compounds using an evaluation function that expresses the binding affinity calculated by a computer. The compounds narrowed down by the virtual screening are verified by biochemical experiments, [5][6][7] and those that are actually determined to be active proceed to the hit-to-lead. Hit-to-Lead is a stage in early drug discovery where small molecule compounds hit by high-throughput screening (HTS) are processed through certain optimizations to identify promising lead compounds.…”

Section: Introductionmentioning

confidence: 94%

MERMAID: An Open Source Automated Hit-to-Lead Method Based on Deep Reinforcement Learning

Erikawa¹,

Yasuo²,

Sekijima

2021

Preprint

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<div>The hit-to-lead process makes the physicochemical properties of the hit compounds that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process.</div><div>The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process.In this study, we have developed a SMILES-based generative model that can be generated starting from a certain compound. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network.We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization.</div><div>The source code is available at https: //github.com/sekijima-lab/mermaid.</div>

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“…Candidate compounds for the assay were selected by SBVS, which is more useful than ligand-based virtual screening (LBVS) for identifying novel scaffolds. [36][37][38][39] The 180 compounds extracted by SBVS were subjected to enzyme inhibition assays to confirm their activity, and six compounds showing activity were obtained.…”

Section: Introductionmentioning

confidence: 99%

Screening for inhibitors of main protease in SARS-CoV-2: in silico and in vitro approach avoiding secondary amides

Sekijima

Yamamoto

Yasuo

2021

Preprint

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In addition to vaccines, antiviral drugs are essential for suppressing COVID-19.Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space excluding secondary amides. In total, 180 compounds were subjected to the primary assay at 20 µM, and nine compounds with inhibition rates higher than 5% were obtained. The IC 50 of six compounds was determined in dose-response experiments, with the values on the order of 10 -4 µM. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in 1 the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.

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A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Cited by 23 publications

References 68 publications

Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors within silicoDriven Library Prioritization and MicroScale Thermophoresis

Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors within silicoDriven Library Prioritization and MicroScale Thermophoresis

MERMAID: An Open Source Automated Hit-to-Lead Method Based on Deep Reinforcement Learning

Screening for inhibitors of main protease in SARS-CoV-2: in silico and in vitro approach avoiding secondary amides

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