On December 31, 2021, Chen, Guzhong; Song, Zhen; Qi, Zhiwen published an article.Product Details of 4719-04-4 The title of the article was Transformer-convolutional neural network for surface charge density profile prediction: Enabling high-throughput solvent screening with COSMO-SAC. And the article contained the following:
A deep learning (DL) method for quickly predicting surface charge d. profiles (σ-profile) and cavity volumes (VCOSMO) of mols. for the COSMO-SAC model is developed. The mol. fingerprints are derived from the encoder state of a Transformer model pre-trained on the ChEMBL database, which allows transfer learning from large-scale unlabeled data and improve generalization performance by developing better mol. fingerprints for building models with significantly smaller datasets. Employing the pre-trained mol. fingerprints, a convolutional neural network (CNN) model for the σ-profile and VCOSMO prediction is trained and tested on the VT-2005 database. The obtained Transformer-CNN model presents superior performance to the GC-COSMO approach and enables the prediction of σ-profile and VCOSMO of millions of mols. in only a few minutes. Taking advantages of the model, a high-throughput solvent screening framework based on COSMO-SAC is further proposed and exemplified by searching sustainable solvent for the deterpenation process of citrus essential oils. The experimental process involved the reaction of 2,2′,2”-(1,3,5-Triazinane-1,3,5-triyl)triethanol(cas: 4719-04-4).Product Details of 4719-04-4
The Article related to deep learning convolutional neural network charge density, History, Education, and Documentation: Education and other aspects.Product Details of 4719-04-4
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