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Abstract MotivationProperties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep-learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently. ResultsHere, we develop a suite of comprehensive machine-learning methods and tools spanning different computational models, molecular representations and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision–recall curves (PRCs) and receiver operating characteristic (ROC) curves. Altogether, our work not only serves as a comprehensive tool, but also contributes toward developing novel and advanced graph and sequence-learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods. In particular, our methods achieve #1 ranking in terms of both ROC-AUC (area under curve) and PRC-AUC on the AI Cures open challenge for drug discovery related to COVID-19. Availability and implementationOur source code is released as part of the MoleculeX library (https://github.com/divelab/MoleculeX) under AdvProp. Supplementary informationSupplementary data are available at Bioinformatics online. 

Cycling, as a green transportation mode, provides an environmentally friendly transportation choice for short-distance traveling. However, cyclists are also getting involved in fatal accidents more frequently in recent years. Thus, understanding and modeling their road behaviors is crucial in helping improving road safety laws and infrastructures. Traditionally, people understand road user behavior using either purely spatial trajectory data, or videos from fixed surveillance camera through tracking or predicting their paths. However, these data only cover limited areas and do not provide information from the cyclist's field of view. In this paper, we take advantage of geo-referenced egocentric video data collected from the handlebar cameras of cyclists to learn how to predict their behaviors. This approach is technically more challenging, because both the observer and objects in the scene might be moving, and there are strong temporal dependencies in both the behaviors of cyclists and the video scenes. We propose Cycling-Net, a novel deep learning model that tracks different types of objects in consecutive scenes and learns the relationship between the movement of these objects and the behavior of the cyclist. Experiment results on a naturalistic trip dataset show the Cycling-Net is effective in behavior prediction and outperforms a baseline model.