FULLERENE GEOMETRY PREDICTION WITH MOLECULAR MECHANICS CALCULATIONS

A serial of fullerenes had been built and the optimized geome- tries had been obtained with the energy minimization of molecular mechanics calculations according to the fact that the pentagonal number is exactly 12 in the fullerenes which consist of pentagons and hexagons.The fullerene geometry prediction could facilitate further theoretical and synthetical studies in the near future.

Modified molecular matrix model for predicting molecular composition of naphtha＇

To improve the naphtha composition prediction model based on molecular type homologous series matrix （MTHS）, this paper puts forward a novel molecular matrix to characterize the naphtha composition and the norreal distribution hypothesis to better describe the molecular composition distribution within each homologous series of the molecular matrix. Through prediction calculation of eight groups of naphtha samples and eight groups of gasoline samples, it is verified that the normal distribution hypothesis is more applicable than gamma distribution hypothesis for the prediction model. According to the prediction results of the samples, the restrain range of normal distribution parameters during model computing process is summarized. With the bulk properties of naphtha samples and the value range of distribution parameters as input conditions, this study utilizes the improved novel molecular matrix to predict the composition of naphtha samples. As the results show, the novel molecular matrix can predict more detailed composition information of naphtha and improve prediction accuracy with less unknown parameters.

Improving the accuracy of pose prediction in molecular docking via structural fltering and conformational clustering

Structure-based virtual screening（molecular docking）is now one of the most pragmatic techniques to leverage target structure for ligand discovery.Accurate binding pose prediction is critical to molecular docking.Here,we describe a general strategy to improve the accuracy of docking pose prediction by implementing the structural descriptor-based fltering and KGS-penalty function-based conformational clustering in an unbiased manner.We assessed our method against 150 high-quality protein–ligand complex structures.Surprisingly,such simple components are suffcient to improve the accuracy of docking pose prediction.The success rate of predicting near-native docking pose increased from 53%of the targets to 78%.We expect that our strategy may have general usage in improving currently available molecular docking programs.

Advanced deep learning methods for molecular property prediction

The prediction of molecular properties is a crucial task in the field of drug discovery.Computational methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug discovery.In recent years,iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property prediction.Deep learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature engineering.in this review,we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction,including state-of-the-art deep learning networks such as graph neural networks and Transformer-based models,as well as state-of-the-art deep learning strategies such as 3D pre-train,contrastive learning,multi-task learning,transfer learning,and meta-learning.We also point out some critical issues such as lack of datasets,low information utilization,and lack of specificity for diseases.