Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target *** recent years,with the development of artificial intelligence(AI),especially ML...
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Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target *** recent years,with the development of artificial intelligence(AI),especially ML,researchers’interest in ML-assisted retrosynthesis planning has rapidly increased,bringing development and opportunities to the *** this review,we aim to provide a comprehensive understanding of ML-assisted retrosynthesis *** first discuss the formal definition and the objective of retrosynthesis planning,and organize a modular framework which includes four modules:data preparation,data preprocessing,pathway generation and evaluation,and pathway ***,we sequentially review the current status of the first three modules(except pathway verification)in the ML-assisted retrosynthesis planning framework,including ideas,methods,and latest *** that,we specifically discuss large language models in retrosynthesis ***,we summarize the extant challenges that are faced by current ML-assisted retrosynthesis planning research and offer a perspective on future research directions and development.
In chemoinformatics, chemical databases have great importance since their main objective is to store and organize the chemical structures of molecules and their properties, from basic information such as chemical stru...
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In chemoinformatics, chemical databases have great importance since their main objective is to store and organize the chemical structures of molecules and their properties, from basic information such as chemical structure to more complex like molecular fingerprints or other types of calculated or experimental descriptors and biological activity. However, this data can only be utilized in projects to identify novel therapeutic molecules or other fields through their correct characterization and analysis. In this Application Note, we compiled five workflows within the open-source data analytics and visualization platform KNIME that can be implemented for the chemoinformatic characterization of databases. To illustrate the application of the workflows, we used BIOFACQUIM, a compound database of natural products isolated and characterized in Mexico [1].
Machine learning is quickly becoming integral to drug discovery pipelines, particularly quantitative structure-activity relationship (QSAR) and absorption, distribution, metabolism, and excretion (ADME) tasks. Graph C...
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Machine learning is quickly becoming integral to drug discovery pipelines, particularly quantitative structure-activity relationship (QSAR) and absorption, distribution, metabolism, and excretion (ADME) tasks. Graph Convolutional Network (GCN) models have proven especially promising due to their inherent ability to model molecular structures using graph-based representations. However, maximizing the potential of such models in practice is challenging, as companies prioritize data privacy and security over collaboration initiatives to improve model performance and robustness. kMoL is an open-source machine learning library with integrated federated learning capabilities developed to address such challenges. Its key features include state-of-the-art model architectures, Bayesian optimization, explainability, and federated learning mechanisms. It demonstrates extensive customization possibilities, advanced security features, straightforward implementation of user-specific models, and high adaptability to custom datasets without additional programming requirements. kMoL is evaluated through locally trained benchmark settings and distributed federated learning experiments using various datasets to assess the features and flexibility of the library, as well as the ability to facilitate fast and practical experimentation. Additionally, results of these experiments provide further insights into the performance trade-offs associated with federated learning strategies, presenting valuable guidance for deploying machine learning models in a privacy-preserving manner within drug discovery pipelines. kMoL is available on GitHub at https://***/elix-tech/*** contribution The primary scientific contribution of this research project is the introduction and evaluation of kMoL, an open-source machine learning library with integrated federated learning capabilities. By demonstrating advanced customization and security capabilities without additional programming requi
Fragment-based screening is an efficient method for early-stage drug discovery. In this study, we aimed to create a fragment library optimized for producing high hit rates against RNA targets. RNA has historically bee...
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Fragment-based screening is an efficient method for early-stage drug discovery. In this study, we aimed to create a fragment library optimized for producing high hit rates against RNA targets. RNA has historically been an underexplored target, but recent research suggests potential for optimizing small molecule libraries for RNA binding. We extended this concept to fragment libraries to produce an RNA optimized fluorinated fragment library. We then screened this library, alongside two non-RNA optimized fragment libraries, against three RNA targets: the human cytoplasmic A-site and the S. cerevisiae tRNAAsp anticodon stem loop with and without nucleobase modifications. The screens yielded 24, 31, and 20 hits against the respective targets. Importantly, statistical analysis confirmed a significant overrepresentation of hits in our RNA optimized library. Based on these findings, we propose guidelines for developing RNA optimized fragment libraries. We hope the guidelines will help expediting fragment-based ligand discovery for RNA targets and contribute to presenting RNA as a promising target in drug discovery.
An acid-base titrator connected to the Internet was developed for conducting remote investigative experiments. The experiment was broadcasted in a high school senior classroom, with the presence of a facilitating teac...
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An acid-base titrator connected to the Internet was developed for conducting remote investigative experiments. The experiment was broadcasted in a high school senior classroom, with the presence of a facilitating teacher. The activity required students to determine the indicator present in the solution. To carry out the activity, students added acid or base to sweep the pH range from 0 to 14 and noted the corresponding coloration to the pH through video analysis. The results obtained were very satisfactory, both in the identification of the indicator and in the acceptance and engagement of the students in the activity.
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