Donghan Wang

The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features. In addition, we utilize MSA to calculate dynamic noise weights to boost molecular conformation noise prediction. Upon the improvements, GADIFF achieves competitive performance compared with recently reported state-of-the-art models in terms of generation diversity(COV-R, COV-P), accuracy (MAT-R, MAT-P), and property prediction for GEOM-QM9 and GEOM-Drugs datasets. In particular, on the GEOM-Drugs dataset, the average COV-R is improved by 3.75% compared with the best baseline model at a threshold (1.25 Å). Furthermore, a transfer model named GADIFF-NCI based on GADIFF is developed to generate conformations for noncovalent interaction (NCI) molecular systems. It takes GADIFF with GEOM-QM9 dataset as a pre-trained model, and incorporates a graph encoder for learning molecular vectors at the NCI molecular level. The resulting NCI molecular conformations are reasonable, as assessed by the evaluation of conformation and property predictions. This suggests that the proposed transferable model may hold noteworthy value for the study of multi-molecular conformations. The code and data of GADIFF is freely downloaded from https://github.com/WangDHg/GADIFF.

The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy. RegNet is a network design paradigm that mainly focuses on local features. Meanwhile, the Vision Transformer is also leveraged for feature extraction, because it can capture global features better than RegNet while lowering the computational cost. Using a transformer encoder as the fusion strategy rather than multilayer perceptron can enhance model performance, due to its emphasis on important features with less parameters. Therefore, the proposed TFRegNCI achieved high accurate prediction (mean absolute error of ∼0.1 kcal/mol) comparing with the coupled cluster single double (triple) (CCSD(T)) benchmark. To further improve the model efficiency, TFRegNCI applies two-dimensional (2D) inputs transformed from three-dimensional (3D) electron density cubes, which saves time (30%), while the model accuracy remains. To improve model interpretability, a visualization module, Gradient-weighted Regression Activation Mapping (Grad-RAM) has been embedded. Grad-RAM is promoted from the classification algorithm, Gradient-weighted Class Activation Mapping, to perform feature visualization for the regression task. With Grad-RAM, the visual location map for features in deep learning models can be displayed. The feature map visualizations suggest that the 2D model has the similar performance as the 3D model, because of equally effective feature extractions from electron density. Moreover, the valid feature region on the location map by the 3D model is consistent with the NCIPLOT NCI isosurface. It is confirmed that the model does extract significant features related to the NCI interaction. The interpretable analyses are carried out through molecular orbital contribution on effective features. Thereby, the proposed model is likely to be a promising tool to reveal some essential information on NCIs, with regard to the level of electronic theory.

The measurement of gas pressure with low detection limit is of great significance and remain challenging. In this work, a gas pressure sensing system with a low detection limit is proposed based on Fabry-Perot interferometer and intracavity sensing of fiber ring laser. A miniature pressure-sensitive fiber-optic Fabry-Perot interferometer is fabricated with polyimide film by a 3D printed resin shell. Then, it is coupled in a fiber ring laser. The reflection loss of the Fabry-Perot interferometer is modulated at different gas pressures. With the intracavity gas pressure sensing induced, the gas pressure can be measured by the output power of the fiber ring laser. The detection limit of the gas pressure sensing system has been greatly optimized from 0.155 KPa to 0.01 KPa. The temperature cross-sensitivity and measurement stability of the gas pressure sensing system are also analyzed. The proposed sensing method can be applied to similar sensors to optimize measurement detection limit.