A Multi-view Molecular Pre-training with Generative Contrastive Learning.

Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previous approaches to learning molecular representations in an end-to-end manner potentially suffered information loss while neglecting the utilization of molecular generative representations. To obtain rich molecular feature information, the pre-training molecular representation model utilized different molecular representations to reduce information loss caused by a single molecular representation. Therefore, we provide the MVGC, a unique multi-view generative contrastive learning pre-training model. Our pre-training framework specifically acquires knowledge of three fundamental feature representations of molecules and effectively integrates them to predict molecular properties on benchmark datasets. Comprehensive experiments on seven classification tasks and three regression tasks demonstrate that our proposed MVGC model surpasses the majority of state-of-the-art approaches. Moreover, we explore the potential of the MVGC model to learn the representation of molecules with chemical significance.

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

Oil/Salt Use Assessment of Chinese-Style Canteens Based on Consumers' Perception of the Nutrition Environment.

Excess cooking oil and salt use in catering services contributes to obesity and cardiovascular disease, but the assessment of oil/salt use has been a challenge in nutrition environment measurement. We conducted a knowledge, attitude, and practice survey on 250 respondents in five university canteens at China Agricultural University, Beijing, China. Using on-site tools including a newly developed Likert scale and the previously tested Oil-Salt Visual Analogue Scale (OS-VAS), the respondents were asked to evaluate their personal taste, their impression of the oil/salt status of canteen dishes, and their attitude toward oil/salt reduction. Data analysis showed that gender and self-image of body shape had a significant impact on KAP scores and the impression of the oil/salt environment. The respondents' taste preferences correlated with their perception of oil and salt, but knowledge and attitude were not directly related to scores on oil and salt, while weight status was related to oil and salt scores. The Likert scale-based assessment could work but was not as effective as the OS-VAS in distinguishing the differences among the selected canteens. These results indicate that the quality of the nutrition environment in catering services needs to be comprehensively evaluated with an objective evaluation of raters and a subjective evaluation of consumers.

NoiseMol: A noise-robusted data augmentation via perturbing noise for molecular property prediction.

Simplified Molecular-Input Line-Entry System (SMILES) is one of a widely used molecular representation methods for molecular property prediction. We conjecture that all the characters in the SMILES string of a molecule are essential for making up the molecules, but most of them make little contribution to determining a particular property of the molecule. Therefore, we verified the conjecture in the pre-experiment. Motivated by the result, we propose to inject proper noisy information into the SMILES to augment the training data by increasing the diversity of the labeled molecules. To this end, we explore injecting perturbing noise into the original labeled SMILES strings to construct augmented data for alleviating the limitation of the labeled compound data and enhancing the model to extract more useful molecular representation for molecular property prediction. Specifically, we directly adopt mask, swap, deletion, and fusion operations on SMILES strings to randomly mask, swap, and delete atoms in SMILES strings. Then, the augmented data is used by two strategies: each epoch alternately feeds the original and perturbing noisy molecules, or each batch alternately feeds the original and perturbing noisy molecules. We conduct experiments on both Transformer and BiGRU models to validate the effectiveness by adopting widely used datasets from MoleculeNet and ZINC. Experimental results demonstrate that the proposed method outperforms strong baselines on all the datasets. NoiseMol obtains the best performance on BBBP and FDA when compared with state-of-the-art methods. Besides, NoiseMol achieves the best accuracy on LogP. Therefore, injecting perturbing noise into the labeled SMILES strings is an effective and efficient method, which improves the prediction performance, generalization, and robustness of the deep learning models.

MolRoPE-BERT: An enhanced molecular representation with Rotary Position Embedding for molecular property prediction.

Molecular property prediction is a significant task in drug discovery. Most deep learning-based computational methods either develop unique chemical representation or combine complex model. However, researchers are less concerned with the possible advantages of enormous quantities of unlabeled molecular data. Since the obvious limited amount of labeled data available, this task becomes more difficult. In some senses, SMILES of the drug molecule may be regarded of as a language for chemistry, taking inspiration from natural language processing research and current advances in pretrained models. In this paper, we incorporated Rotary Position Embedding(RoPE) efficiently encode the position information of SMILES sequences, ultimately enhancing the capability of the BERT pretrained model to extract potential molecular substructure information for molecular property prediction. We proposed the MolRoPE-BERT framework, an new end-to-end deep learning framework that integrates an efficient position coding approach for capturing sequence position information with a pretrained BERT model for molecular property prediction. To generate useful molecular substructure embeddings, we first exclusively train the MolRoPE-BERT on four million unlabeled drug SMILES(i.e., ZINC 15 and ChEMBL 27). Then, we conduct a series of experiments to evaluate the performance of our proposed MolRoPE-BERT on four well-studied datasets. Compared with conventional and state-of-the-art baselines, our experiment demonstrated comparable or superior performance.

A deep learning method for predicting molecular properties and compound-protein interactions.

Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp.