In the drug development process, mouse liver microsomal (MLM) studies are an invaluable biological assay used to assess the metabolic stability of novel drug candidates prior to human studies. However, determining MLM stability, in addition to other absorption, distribution, metabolism, and excretion (ADME) properties, can be a time-intensive and expensive process if it were tested in many compounds, thus leading to the need to create computational models capable of predicting properties of novel compounds. Additionally, building accurate computational models for the prediction of MLM stability can greatly accelerate the screening process for the selection of an appropriate drug candidate and further reduce the failure rate of the compounds in later trial stages. Our study outlined within this paper will discuss the history of computational models and their ability to predict MLM stability using traditional machine learning methods, as well as discuss a novel deep learning architecture, graph convolutional neural networks, capable of stronger predictive capabilities when compared to traditional methods. With future advances in hardware and research, deep learning methods applied to the prediction of ADME properties including but not limited to microsomal stability prediction represent an invaluable tool for future drug discovery efforts in both industry and academic settings.

We present a study on the use of lexical stress classication to aid in the recognition of phonetically similar words. In this study, w e use a simple pattern recognition approach t o determine which syllable is lexically stressed for phonetically similar word pairs (e.g., PERfect, perFECT) extracted from continuously spoken sentences. We use a combination of two features from the acoustic correlates of lexical stress, and assume multivariate Gaussian distributions to form a Bayesian classier. The features used are normalized energy and duration of the vowel for each syllable of the word. We e v aluate several normalization methods. Two sets of sentences were designed for this study. F or the pilot experiment, the classication accuracy on words from the natural sentence set was 89.9% and on words from the control sentence set was 100%. To improve the performance, three-feature classiers, which included two normalized energy features and one normalized duration feature, were developed. The classication accuracy on words from the natural sentence set was 97.23%.

No abstract

No abstract

No abstract