Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.

TransDFL:Identification of Disordered Flexible Linkers in Proteins by Transfer Learning

Disordered flexible linkers(DFLs)are the functional disordered regions in proteins,which are the sub-regions of intrinsically disordered regions(IDRs)and play important roles in connecting domains and maintaining inter-domain interactions.Trained with the limited available DFLs,the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs,leading to a high false positive rate(FPR)and low prediction accuracy.Previous studies have shown that DFLs are extremely flexible disordered regions,which are usually predicted as disordered residues with high confidence[P(D)>0.9]by an IDR predictor.Therefore,transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs.In this study,we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction.The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs,which is helpful to reduce the false positives in the ordered regions.RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL.Experimental results of two application scenarios(prediction of DFLs only in IDRs or prediction of DFLs in entire proteins)showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy.

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft.The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition(EMD) and Soft Thresholding(TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.