The recently proposed Genetic expert guided learning (GEGL) framework has demonstrated impressive performances on several \textit{de novo} molecular design tasks. Despite the displayed state-of-the art results, the proposed system relies on an expert-designed Genetic expert. Although hand-crafted experts allow to navigate the chemical space efficiently, designing such experts requires a significant amount of effort and might contain inherent biases which can potentially slow down convergence or even lead to sub-optimal solutions. In this research, we propose a novel genetic expert named \textit{InFrag} which is free of design rules and can generate new molecules by combining promising molecular fragments. Fragments are obtained by using an additional graph convolutional neural network which computes attributions for each atom for a given molecule. Molecular substructures which contribute positively to the task score are kept and combined to propose novel molecules. We experimentally demonstrate that, within the GEGL framework, our proposed attribution-based genetic expert is either competitive or outperforms the original expert-designed genetic expert on goal-directed optimization tasks. When limiting the number of optimization rounds to one and three rounds, a performance increase of approximately 43% and 20% respectively is observed compared to the baseline genetic expert. Furthermore, we empirically show that combining several experts that share a fixed sampling budget at each optimization round generally improves or maintains the overall performance of the framework.
To address the remaining issue of poor cell immobilization and insufficient mass transfer in scaffold-based tissue engineering approach for future islet transplantation, we employed a macro-porous poly-l-lactide (PLLA) scaffold immobilizing mouse insulinoma cells and studied its function toward an implantable pancreatic tissue in 7-day perfusion culture. The murine pancreatic β cells could be immobilized in the PLLA scaffold at a high density of 107 cells per cm3 close to the estimated range in normal pancreas. The perfusion culture promoted the 3D cellular organization as observed with live/dead staining and histological staining. The insulin production was significantly enhanced in comparison with static 2D culture and 3D rotational suspension culture by two and six folds, respectively ( p < 0.001). As enhanced insulin response was only observed where both the perfusion and 3D cellular organization were present, this could represent important elements in engineering a functional bioartificial pancreas.
The recently proposed Genetic expert guided learning (GEGL) framework has demonstrated impressive performances on several \textit{de novo} molecular design tasks. Despite the displayed state-of-the art results, the proposed system relies on an expert-designed Genetic expert. Although hand-crafted experts allow to navigate the chemical space efficiently, designing such experts requires a significant amount of effort and might contain inherent biases which can potentially slow down convergence or even lead to sub-optimal solutions. In this research, we propose a novel genetic expert named \textit{InFrag} which is free of design rules and can generate new molecules by combining promising molecular fragments. Fragments are obtained by using an additional graph convolutional neural network which computes attributions for each atom for a given molecule. Molecular substructures which contribute positively to the task score are kept and combined to propose novel molecules. We experimentally demonstrate that, within the GEGL framework, our proposed attribution-based genetic expert is either competitive or outperforms the original expert-designed genetic expert on goal-directed optimization tasks. When limiting the number of optimization rounds to one and three rounds, a performance increase of approximately 43% and 20% respectively is observed compared to the baseline genetic expert. Furthermore, we empirically show that combining several experts that share a fixed sampling budget at each optimization round generally improves or maintains the overall performance of the framework.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.