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Keywords = GCPN

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16 pages, 2405 KiB  
Article
Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework
by Xiangying Zhang, Haotian Gao, Yifei Qi, Yan Li and Renxiao Wang
Molecules 2025, 30(1), 18; https://doi.org/10.3390/molecules30010018 - 24 Dec 2024
Viewed by 1360
Abstract
As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models [...] Read more.
As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literature, which have completely redefined the concept of de novo drug design. However, many of them lack practical value for real-world drug discovery. In this work, we have developed a graph-based generative model within a reinforcement learning framework, namely, METEOR (Molecular Exploration Through multiplE-Objective Reinforcement). The backend agent of METEOR is based on the well-established GCPN model. To ensure the overall quality of the generated molecular graphs, we implemented a set of rules to identify and exclude undesired substructures. Importantly, METEOR is designed to conduct multi-objective optimization, i.e., simultaneously optimizing binding affinity, drug-likeness, and synthetic accessibility of the generated molecules under the guidance of a special reward function. We demonstrate in a specific test case that without prior knowledge of true binders to the chosen target protein, METEOR generated molecules with superior properties compared to those in the ZINC 250k data set. In conclusion, we have demonstrated the potential of METEOR as a practical tool for generating rational drug-like molecules in the early phase of drug discovery. Full article
(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)
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12 pages, 295 KiB  
Article
The Diagnosability of the Generalized Cartesian Product of Networks
by Meirun Chen and Cheng-Kuan Lin
Mathematics 2023, 11(12), 2615; https://doi.org/10.3390/math11122615 - 7 Jun 2023
Cited by 4 | Viewed by 1438
Abstract
Motivated by two typical ways to construct multiprocessor systems, matching composition networks and cycle composition networks, we generalize the definition of the Cartesian product of networks and consider the classical diagnosability of the generalized Cartesian product of networks (GCPNs). In this paper, we [...] Read more.
Motivated by two typical ways to construct multiprocessor systems, matching composition networks and cycle composition networks, we generalize the definition of the Cartesian product of networks and consider the classical diagnosability of the generalized Cartesian product of networks (GCPNs). In this paper, we determine the accurate value of the classical diagnosability of the generalized Cartesian product of networks (GCPNs) under the PMC model and the MM* model. Full article
(This article belongs to the Special Issue Advances of Computer Algorithms and Data Structures)
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