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Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neural networks (CNNs) and graph neural networks, effective and efficient representations that characterize the structural, physical, chemical and biological properties of molecular structures and interactions remain to be a great challenge. Here, we propose an equal-sized molecular 2D image representation, known as the molecular persistent spectral image (Mol-PSI), and combine it with CNN model for AI-based drug design. Mol-PSI provides a unique one-to-one image representation for molecular structures and interactions. In general, deep models are empowered to achieve better performance with systematically organized representations in image format. A well-designed parallel CNN architecture for adapting Mol-PSIs is developed for protein–ligand binding affinity prediction. Our results, for the three most commonly used databases, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, are better than all traditional machine learning models, as far as we know. Our Mol-PSI model provides a powerful molecular representation that can be widely used in AI-based drug design and molecular data analysis. [ABSTRACT FROM AUTHOR]

As an important reversible lipid modification, S -palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, computational prediction of S -palmitoylation sites can efficiently generate helpful candidates for further experimental consideration. Here, we reviewed the current progress in the development of S -palmitoylation site predictors, as well as training data sets, informative features and algorithms used in these tools. Then, we compiled a benchmark data set containing 3098 known S -palmitoylation sites identified from small- or large-scale experiments, and developed a new method named data quality discrimination (DQD) to distinguish data quality weights (DQWs) between the two types of the sites. Besides DQD and our previous methods, we encoded sequence similarity values into images, constructed a deep learning framework of convolutional neural networks (CNNs) and developed a novel algorithm of graphic presentation system (GPS) 6.0. We further integrated nine additional types of sequence-based and structural features, implemented parallel CNNs (pCNNs) and designed a new predictor called GPS-Palm. Compared with other existing tools, GPS-Palm showed a >31.3% improvement of the area under the curve (AUC) value (0.855 versus 0.651) for general prediction of S -palmitoylation sites. We also produced two species-specific predictors, with corresponding AUC values of 0.900 and 0.897 for predicting human- and mouse-specific sites, respectively. GPS-Palm is free for academic research at http://gpspalm.biocuckoo.cn/. [ABSTRACT FROM AUTHOR]

Different from neurons in the peripheral nervous system, mature neurons in the mammalian central nervous system often fail to regenerate after injury. Recent studies have found that calcium transduction, injury signaling, mitochondrial transportation, cytoskeletal remodeling and protein synthesis play essential roles in axon regeneration. Firstly, axon injury increases the intracellular concentration of calcium, and initiates the injury signaling pathways including cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) and dual leucine kinase (DLK), which are found to promote axon regeneration in multiple animal injury models. The second step for axonal regrowth is to rebuild growth cones. Overexpressing proteins that promote dynamics of microtubules and actin filaments is beneficial for the reassembly of cytoskeletons and initiation of new growth cones. Thirdly, mitochondria, the power factory for cells, also play important roles in growth cone formation and axonal extension. The last but not the least important step is the regulation of gene transcription and protein translation to sustain the regrowth of axons. This review summarizes important findings revealing the functions and mechanisms of these biological progresses.