Peach (Prunus persica L.) is a highly valuable crop species and is recognized by molecular researchers as a model fruit for the Rosaceae family. Using whole-genome sequencing data generated from 129 peach accessions, here we perform a comprehensive genome-wide association study for 12 key agronomic traits. We show that among the 10 qualitative traits investigated, nine exhibit consistent and more precise association signals than previously identified by linkage analysis. For two of the qualitative traits, we describe candidate genes, one potentially involved in cell death and another predicted to encode an auxin-efflux carrier, that are highly associated with fruit shape and non-acidity, respectively. Furthermore, we find that several genomic regions harbouring association signals for fruit weight and soluble solid content overlapped with predicted selective sweeps that occurred during peach domestication and improvement. Our findings contribute to the large-scale characterization of genes controlling agronomic traits in peach.

We report the design and synthesis of small-sized zwitterion-coated gadolinium-embedded iron oxide (GdIO) nanoparticles, which exhibit a strong T1 contrast effect for tumor imaging through enhanced permeation and retention effect and the ability to clear out of the body in living subjects. The combination of spin-canting effects and the collection of gadolinium species within small-sized GdIO nanoparticles led to a significantly enhanced T1 contrast effect. For example, GdIO nanoparticles with a diameter of ~4.8 nm exhibited a high r1 relaxivity of 7.85 mM−1 · S−1 and a low r2/r1 ratio of 5.24. After being coated with zwitterionic dopamine sulfonate molecules, the 4.8 nm GdIO nanoparticles showed a steady hydrodynamic diameter (~5.2 nm) in both PBS buffer and fetal bovine serum solution, indicating a low nonspecific protein absorption. This study provides a valuable strategy for the design of highly sensitive iron-oxide-based T1 contrast agents with relatively long circulation half-lives (~50 min), efficient tumor passive targeting (SKOV3, human ovarian cancer xenograft tumor as a model), and the possibility of rapid renal clearance after tumor imaging.

Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand-target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.