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10.1038/s41598-025-24757-3 http://scihub22266oqcxt.onion/10.1038/s41598-025-24757-3 C12550069!12550069 !41131333     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41131333 &cmd=llinks): Failed to open stream: HTTP request failed! 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Automated parametrization of small molecules within the Martini 3 coarse-grained model guided by experimental log P values |pdf=|usr=}}{{41131333 }} gab.com Text Automated parametrization of small molecules within the Martini 3 coarse-grained model guided by experimental log P values JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=41131333 #FOAvip Molecular dynamics simulations play an important role in investigating biological systems. However, simulating large-scale systems can be computationally expensive, which can be improved by the employment of a coarse-graining force field. This study focuses on the automated parametrization of small molecules within the CGCompiler framework. This optimization approach utilizes a mixed-variable particle swarm algorithm to avoid the manual tweaking of parameters. Particularly, the optimization focuses on matching experimentally known log P values of partitioning in water-octanol phases, reproducing atomistic density profiles in lipid bilayers, and optimizing overall shape and volume aspects of the modeled atomistic molecules. After the atomistic to coarse-grained mapping, the model's accuracy is evaluated through a fitness function, which combines structural and dynamic targets, to accurately capture the shape and behavior of the small molecule in question. Through the investigation of the interactions between small molecules and cellular membranes, this optimization process supports the development of accurate coarse-grained models for small molecules relevant to drug discovery. Our work demonstrates promising results in automating the high-fidelity parametrization of small molecules using the Martini 3 force-field guided by experimental log P values. Twit Text FOAVip Automated parametrization of small molecules within the Martini 3 coarse-grained model guided by experimental log P values ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=41131333 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41131333 #FOAvip English Wikipedia <ref>{{Cite pmid|41131333 }}</ref> Automated parametrization of small molecules within the Martini 3 coarse-grained model guided by experimental log P values #MMPMID41131333 Kelidou M ; Stroh KS ; Risselada HJ Sci Rep 2025[Oct]; 15 (1 ): 37169 PMID41131333 show ga Molecular dynamics simulations play an important role in investigating biological systems. However, simulating large-scale systems can be computationally expensive, which can be improved by the employment of a coarse-graining force field. This study focuses on the automated parametrization of small molecules within the CGCompiler framework. This optimization approach utilizes a mixed-variable particle swarm algorithm to avoid the manual tweaking of parameters. Particularly, the optimization focuses on matching experimentally known log P values of partitioning in water-octanol phases, reproducing atomistic density profiles in lipid bilayers, and optimizing overall shape and volume aspects of the modeled atomistic molecules. After the atomistic to coarse-grained mapping, the model's accuracy is evaluated through a fitness function, which combines structural and dynamic targets, to accurately capture the shape and behavior of the small molecule in question. Through the investigation of the interactions between small molecules and cellular membranes, this optimization process supports the development of accurate coarse-grained models for small molecules relevant to drug discovery. Our work demonstrates promising results in automating the high-fidelity parametrization of small molecules using the Martini 3 force-field guided by experimental log P values. äAutomated parametrization of small molecules within the Martini 3 coar(epmc).pdf DeepDyve Pubget Overpricing