Effects of Low Temperature Stress on Germination and Physiological Characteristics of Different Sweet Maize Varieties

[Objectives]The paper was to explore the effects of low temperature stress on germination and physiological characteristics of different sweet maize varieties.[Methods]Taking Taitian 264,Zhexuetian 1 and Chaotian 4 as the research objects,the changes in germination potential,germination index,plant height,biomass,and antioxidant enzyme activity of maize seeds were studied under optimal temperature conditions(25℃)and low temperature stress conditions(10℃).[Results]Under 10℃stress,the germination rate and germination index of Taitian 264 were higher than that of Zhexuetian 1 and Chaotian 4.Under low temperature stress,Taitian 264 exhibited the least reduction in height and biomass,while Zhexuetian 1 had the most reduction.Additionally,the SOD and POD activities of Taitian 264 were higher than that of Zhexuetian 1 and Chaotian 4 under both temperature conditions,while the MDA content of Taitian 264 was lower.Taitian 264 showed strong germination ability against low temperature stress.[Conclusions]This study provides a basis for timely sowing practices of sweet maize in agricultural production.

Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning

Deep learning(DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials.However,the progress of many DL-assisted synthesis planning(DASP)algorithms has suffered from the lack of reliable automated pathway evaluation tools.As a critical metric for evaluating chemical reactions,accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios.Currently,accurately predicting yields of interesting reactions still faces numerous challenges,mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors.To compensate for the limitations of high-throughput yield datasets,we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information.Subsequently,by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning,we proposed a powerful bidirectional encoder representations from transformers(BERT)-based reaction yield predictor named Egret.It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset.We found that reaction-condition-based contrastive learning enhances the model’s sensitivity to reaction conditions,and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions.Furthermore,we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes.Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules.In addition,through meta-learning strategy,we further improved the reliability of the model’s prediction for reaction types with limited data and lower data quality.Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.

Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center

Effective synthesis planning powered by deep learning(DL)can significantly accelerate the discovery of new drugs and materials.However,most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions(RCs)for their reaction predictions.Currently,the prediction of RCs with a DL framework is hindered by several factors,including:(a)lack of a standardized dataset for benchmarking,(b)lack of a general prediction model with powerful representation,and(c)lack of interpretability.To address these issues,we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot.Through careful design of the model architecture,pretraining method,and training strategy,Parrot improved the overall top-3 prediction accuracy on catalysis,solvents,and other reagents by as much as 13.44%,compared to the best previous model on a newly curated dataset.Additionally,the mean absolute error of the predicted temperatures was reduced by about 4℃.Furthermore,Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy.Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs.The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable,generalizable,and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.

MF-SuP-pK_(a): Multi-fidelity modeling with subgraph pooling mechanism for pK_(a) prediction

Acid-base dissociation constant(pK_(a)) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_(a) prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_(a)(multi-fidelity modeling with subgraph pooling for pK_(a) prediction), a novel pK_(a) prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledgeaware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_(a) prediction. To overcome the scarcity of accurate pK_(a) data, lowfidelity data(computational pK_(a)) was used to fit the high-fidelity data(experimental pK_(a)) through transfer learning. The final MF-SuP-pK_(a) model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_(a) achieves superior performances to the state-of-theart pK_(a) prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_(a) achieves 23.83% and 20.12% improvement in terms of mean absolute error(MAE) on the acidic and basic sets, respectively.

Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development,such as lead discovery,drug repurposing and elucidation of potential drug side effects.Therefore,a variety of machine learning-based models have been developed to predict these interactions.In this study,a model called auxiliary multi-task graph isomorphism network with uncertainty weighting(AMGU)was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network(MT-GIN)with the auxiliary learning and uncertainty weighting strategy.The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks(GNN)models on the internal test set.Furthermore,it also exhibited much better performance on two external test sets,suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity.Then,a naÏve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms,and the consistency of the interpretability results for 5typical epidermal growth factor receptor(EGFR)inhibitors with their structure-activity relationships could be observed.Finally,a free online web server called KIP was developed to predict the kinomewide polypharmacology effects of small molecules(http://cadd.zju.edu.cn/kip).

Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network

Covalent ligands have attracted increasing attention due to their unique advantages,such as long residence time,high selectivity,and strong binding affinity.They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed.However,our limited knowledge of covalent binding sites has hindered the discovery of novel ligands.Therefore,developing in silico methods to identify covalent binding sites is highly desirable.Here,we propose DeepCoSI,the first structure-based deep graph learning model to identify ligandable covalent sites in the protein.By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment,DeepCoSI achieves state-of-the-art predictive performances.The validation on two external test sets which mimic the real application scenarios shows that DeepCosI has strong ability to distinguish ligandable sites from the others.Finally,we profiled the entire set of protein structures in the RCSB Protein Data Bank(PDB)with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design,and made the predicted data publicly available on website.