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Target identification for bioactive molecules augments modern drug discovery efforts in a range of applications, from the elaboration of mode‐of‐action of drugs to the drug repurposing to even the knowledge of side‐effects and further optimization. However, the traditional labor‐intensive and time‐consuming experiment methods obstructed the development. Driven by massive bioactivity data deposited in chemogenomic databases, computational alternatives have been proposed and widely developed to expedite the validation process. By screening a compound against a protein database, it is possible to identify potential target candidates that fit with this specific compound for subsequent experimental validation. In particular, ligand‐based target prediction methods have made tremendous progress in the past decade due to their flexibility, relatively low computational cost, and remarkable predictive performance, and are still moving forward. In this review, we present a comprehensive overview of ligand‐based target prediction methods including similarity searching, machine learning and algorithm stacking, and the strategies to validate these methods. We also discuss the strength and weakness of the existing data sources for model development and outline the challenges and prospects of ligand‐based target prediction. It is expected that the topic discussed in this review should guide the development and application of ligand‐based target prediction and be of interest to the audiences for wider scientific community. This article is categorized under: Data Science > Chemoinformatics

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Late spring coldness (LSC) is critical for wheat growth and development in the Huang-Huai valleys of China. However, little is known about the molecular mechanisms for young spikes responding to low temperature (LT) stress during anther connective tissue formation phase (ACFP). To elucidate the molecular mechanisms associated with low temperature, we performed a comparative transcriptome analysis of wheat cultivars Xinmai26 (XM26: cold-sensitive) and Yannong19 (YN19: cold-tolerant) using RNA-seq data. Over 4000 differently expressed genes (DEGs) were identified under low temperature conditions (T1: 4°C) and freezing conditions (T2: −4°C) compared with control (CK: 16°C). The number of DEGs associated with two cultivars at two low temperature treatments (T1: 4°C and T2: −4°C) were 834, 1,353, 231, and 1,882 in four comparison groups (Xinmai26-CK vs. Xinmai26-T1, Xinmai26-CK vs. Xinmai26-T2, Yannong19-CK vs. Yannong19-T1, and Yannong19-CK vs. Yannong19-T2), respectively. Furthermore, to validate the accuracy of RNA-seq, 16 DEGs were analyzed using quantitative real-time RT-PCR. Several transcriptome changes were observed through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analysis in plant hormone signal transduction, circadian rhythm-plant, and starch and sucrose metabolism under low temperature. In addition, 126 transcription factors (TFs), including AP2-ERF, bHLH, WRKY, MYB, HSF, and members of the bZIP family, were considered as cold-responsive. It is the first study to investigate DEGs associated with low temperature stress at the transcriptome level in two wheat cultivars with different cold resistance capacities. Most likely, the variations in transcription factors (TFs) regulation, and starch and sucrose metabolism contribute to different cold resistance capacities in the two cultivars. Further, physiological activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) enzymes, malondialdehyde (MDA), soluble sugar (SS), and sucrose contents were evaluated to investigate the negative impacts of low temperature in both cultivars. These findings provide new insight into the molecular mechanisms of plant responses to low temperature and potential candidate genes that required for improving wheat’s capacity to withstand low temperature stress.

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