Teruki Honma's research while affiliated with RIKEN and other places
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Publications (9)
Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial in...
One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public–private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public–private partnerships using our initiative in Japan...
Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies a criteria of various objectives, such as selectivity for a target protein, pharmacokinetics endpoints, and drug-like indices. Recent breakthroughs in artificial...
Designing highly selective molecules for a drug target protein against off-targets, including pharmacokinetics and toxicity-related proteins, is a challenging task in drug discovery and can be regarded as a multiobjective problem. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation meth...
A novel framework for a public–private (PP) partnership was established by a national initiative of the Development of a Drug Discovery Informatics System, supported by the Japan Agency for Medical Research and Development (AMED). This informatics PP partnership consortium comprised private and public sectors. A database of pharmacokinetic (PK) and...
Abstract Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilizatio...
div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of...
Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this...
We propose a novel force‐field‐parametrization procedure that fits the parameters of potential functions in a manner that the pair distribution function (DF) of molecules derived from candidate parameters can reproduce the given target DF. Conventionally, approaches to minimize the difference between the candidate and target DFs employ radial DFs (...
Citations
... Despite its computationally intensive nature and the challenge of striking the right balance, MCTS is indispensable due to its proficiency in navigating vast and complex molecular landscapes. It not only assists in the discovery and design of potential drug candidates but can also customize drugs to bind to specific targets [63][64][65]. MCTS particularly shines in retrosynthetic planning by offering a systematic approach to deconstructing complex organic molecules, thereby streamlining the planning of synthetic routes. By exploring a multitude of synthetic pathways, it helps chemists plan and execute synthesis more efficiently [66]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/939123300921344-1600915851220_Q64/Shoichi-Ishida.jpg)
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... The most important factor to create accurate prediction models is to collect high-quality experimental data as much as possible. To this end, we have established a consortium with seven pharmaceutical companies and obtained chemical descriptors and experimental data for several pharmacokinetic parameters from these companies, 25,26 although these data are proprietary to the companies and we cannot provide them in DruMAP. Another approach to increase experimental data is to collect data from documents on new drug applications, such as Common Technical Document (CTD), package inserts, or Interview Form (IF), which is a supplementary document of the package insert of a launched drug specifically in Japan. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/583638261440514-1516161613548_Q64/Kenji-Mizuguchi.jpg)
... The most important factor to create accurate prediction models is to collect high-quality experimental data as much as possible. To this end, we have established a consortium with seven pharmaceutical companies and obtained chemical descriptors and experimental data for several pharmacokinetic parameters from these companies, 25,26 although these data are proprietary to the companies and we cannot provide them in DruMAP. Another approach to increase experimental data is to collect data from documents on new drug applications, such as Common Technical Document (CTD), package inserts, or Interview Form (IF), which is a supplementary document of the package insert of a launched drug specifically in Japan. ...
... Additionally, GCN can combine different types of information, such as the chemical structures of molecules and amino acid sequences of proteins, through multimodal learning. A recent study applied multimodal GCN to the classification of molecular properties (36). However, the effectiveness of multimodal GCN in predicting ADME properties, including transporter activity, remains unclear. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/823954700779520-1573457517505_Q64/Ryosuke-Kojima-2.jpg)
... Earlier definition of CG particles are rather ad hoc [20]. More formulations with improved statistical mechanical rigor appeared later on [22], with radial distribution function based inversion [78,[84][85][86], entropy divergence [19] and force matching algorithm [87][88][89] being outstanding examples of systematic development. Present CG is essentially to realize the following mapping as disclosed by Equation (4) in ref. [22] : ...
