Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.

Physical and mental health characteristics related to trust in and intention to receive COVID-19 vaccination: results from a Korean community-based longitudinal study

OBJECTIVES

The aim of this study was to explore factors affecting attitudes toward coronavirus disease 2019 (COVID-19) vaccination, including socio-demographic characteristics and mental health status during the pandemic.

METHODS

This study analyzed responses from 1,768 participants who were originally included in a community cohort study and responded to 3 online surveys related to COVID-19 (March 2020 to March 2021). The k-means method was used to cluster trust in and intention to receive COVID-19 vaccination. Baseline (2013-2018) socio-demographic characteristics, physical health status, and depressive symptoms were analyzed as exposure variables, and current mental health status was included in the analyses.

RESULTS

Almost half of all participants were classified into the moderate trust and high intention cluster (n=838, 47.4%); those with high trust and high intention accounted only for 16.9%. They tended to be older, had high-income levels, and engaged in regular physical activity at baseline (p<0.05), and their sleep quality and psychological resilience were relatively high compared to other groups.

CONCLUSIONS

Our results suggest that more efforts are required to enhance the perceived need for and trust in COVID-19 vaccination.

Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19

Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.

A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers

With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver’s distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver’s actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver’s action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems.

A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image

The star tracker is widely used for high-accuracy missions due to its high accuracy position high autonomy and low power consumption. On the other hand, the ability of interference suppression of the star tracker has always been a hot issue of concern. A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences. Firstly, the restricted LC (luminance-based contrast) transformation is utilized to enhance the contrast between background noise and the large-area interference. Then, SLIC (the simple linear iterative clustering) algorithm is adopted to segment the saliency map and in this process, optimized parameters are harnessed. Finally, from these segments, features are extracted and superpixels with similar features are combined by using DBSCAN (density-based spatial clustering of applications with noise). The proposed algorithm is proved effective by successfully removing large-area interference and extracting star spots from the sky region of the real star image.

A Fuzzy Relational Clustering Algorithm Based on a Dissimilarity Measure Extracted From Data

One of the critical aspects of clustering algorithms is the correct identification of the dissimilarity measure used to drive the partitioning of the data set. The dissimilarity measure induces the cluster shape and therefore determines the success of clustering algorithms. As cluster shapes change from a data set to another, dissimilarity measures should be extracted from data. To this aim, we exploit some pairs of points with known dissimilarity value to teach a dissimilarity relation to a feed-forward neural network. Then, we use the neural dissimilarity measure to guide an unsupervised relational clustering algorithm. Experiments on synthetic data sets and on the Iris data set show that the relational clustering algorithm based on the neural dissimilarity outperforms some popular clustering algorithms (with possible partial supervision) based on spatial dissimilarity.