From bedding to bedside: genetically engineered mouse models of cancer inform concurrent clinical trials

Deep Learning Based Drug Metabolites Prediction

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

Clinicopathological characterization of mouse models of melanoma

Mouse models of melanoma have proven invaluable in the delineation of key molecular events involved in disease progression in humans and provide potential preclinical models for therapeutic testing (Damsky and Bosenberg, Pigment Cell Melanoma Res 25(4):404-405, 2012; Walker et al., Pigment Cell Melanoma Res 24(6):1158-1176, 2011). Here we concentrate on the clinicopathological analysis of melanocytic tumors.