Graph-based molecular Pareto optimization was written by Verhellen, Jonas. And the article was included in Chemical Science in 2022.Application of 83799-24-0 The following contents are mentioned in the article:
Computer-assisted design of small mols. has experienced a resurgence in academic and industrial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate mols. that fulfil required chem. properties is encouraging, the use of deep learning models requires significant, if not prohibitive, amounts of data and computational power. At the same time, open-sourcing of more traditional techniques such as graph-based genetic algorithms for mol. optimization [Jensen, Chem. Sci., 2019, 12, 3567-3572] has shown that simple and training-free algorithms can be efficient and robust alternatives. Further research alleviated the common genetic algorithm issue of evolutionary stagnation by enforcing mol. diversity during optimization [Van den Abeele, Chem. Sci., 2020, 42, 11485-11491]. The crucial lesson distilled from the simultaneous development of deep generative models and advanced genetic algorithms has been the importance of chem. space exploration [Aspuru-Guzik, Chem. Sci., 2021, 12, 7079-7090]. For single-objective optimization problems, chem. space exploration had to be discovered as a useable resource but in multi-objective optimization problems, an exploration of trade-offs between conflicting objectives is inherently present. In this paper we provide state-of-the-art and open-source implementations of two generations of graph-based non-dominated sorting genetic algorithms (NSGA-II, NSGA-III) for mol. multi-objective optimization. We provide the results of a series of benchmarks for the inverse design of small mol. drugs for both the NSGA-II and NSGA-III algorithms. In addition, we introduce the dominated hypervolume and extended fingerprint based internal similarity as novel metrics for these benchmarks. By design, NSGA-II, and NSGA-III outperform a single optimization method baseline in terms of dominated hypervolume, but remarkably our results show they do so without relying on a greater internal chem. diversity. This study involved multiple reactions and reactants, such as 2-(4-(1-Hydroxy-4-(4-(hydroxydiphenylmethyl)piperidin-1-yl)butyl)phenyl)-2-methylpropanoic acid (cas: 83799-24-0Application of 83799-24-0).
2-(4-(1-Hydroxy-4-(4-(hydroxydiphenylmethyl)piperidin-1-yl)butyl)phenyl)-2-methylpropanoic acid (cas: 83799-24-0) belongs to piperidine derivatives. The piperidine moiety constitutes an important building block for the synthesis of a variety of bioactive natural products, alkaloids and other drugs. Piperidine derivatives bearing a masked aldehyde function in the ε-position are easily transformed into quinolizidine compounds through intramolecular reductive amination.Application of 83799-24-0
Referemce:
Piperidine – Wikipedia,
Piperidine | C5H11N – PubChem