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Escaping from Flatland: Substituted Bridged Pyrrolidine Fragments with Inherent Three-Dimensional Character.

ABSTRACT: The pressure to deliver new medicines to the patient continues to grow along with increases in compound failure rate, thus putting the current R&D model at risk. Analysis has shown that increasing the three-dimensionality of potential drug candidates decreases the risk of failure and improves binding selectivity and frequency. For this reason many workers have taken a new look at the power of photochemistry as a means to generate novel sp³ rich scaffolds for use in drug discovery programs. Here we report the design, synthesis, and computational structural analysis of a series of 2,4-methanoprolines having inherent 3D character (PMI and PBF scores) significantly higher than that of the broader AbbVie Rule of 3 (Ro3) collection.

SUBMITTER: Cox B

PROVIDER: S-EPMC7294729 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMethods based on the combination of transformer and convolutional neural networks (CNNs) have achieved impressive results in the field of medical image segmentation. However, most of the recently proposed combination segmentation approaches simply treat transformers as auxiliary modules which help to extract long-range information and encode global context into convolutional representations, and there is a lack of investigation on how to optimally combine self-attention with convolution.MethodsWe designed a novel transformer block (MRFormer) that combines a multi-head self-attention layer and a residual depthwise convolutional block as the basic unit to deeply integrate both long-range and local spatial information. The MRFormer block was embedded between the encoder and decoder in U-Net at the last two layers. This framework (UMRFormer-Net) was applied to the segmentation of three-dimensional (3D) pancreas, and its ability to effectively capture the characteristic contextual information of the pancreas and surrounding tissues was investigated.ResultsExperimental results show that the proposed UMRFormer-Net achieved accuracy in pancreas segmentation that was comparable or superior to that of existing state-of-the-art 3D methods in both the Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) dataset and the public Medical Segmentation Decathlon dataset (self-division). UMRFormer-Net statistically significantly outperformed existing transformer-related methods and state-of-the-art 3D methods (P<0.05, P<0.01, or P<0.001), with a higher Dice coefficient (85.54% and 77.36%, respectively) or a lower 95% Hausdorff distance (4.05 and 8.34 mm, respectively).ConclusionsUMRFormer-Net can obtain more matched and accurate segmentation boundary and region information in pancreas segmentation, thus improving the accuracy of pancreas segmentation. The code is available at https://github.com/supersunshinefk/UMRFormer-Net.

Dataset Information

Escaping from Flatland: Substituted Bridged Pyrrolidine Fragments with Inherent Three-Dimensional Character.

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