Self-defense by avoiding self-targeting: By silencing transposons, Piwi-interacting RNAs (piRNAs) protect the stability of animal genomes in germ lines. However, many piRNAs do not map to transposons, and their functions have remained undefined. Zhang et al. described the piRNA targeting logic in Caenorhabditis elegans and identified an intrinsic sequence signal in endogenous germline genes that confer resistance to piRNA silencing. Thus, diverse piRNAs silence foreign nucleic acids but spare self genes to defend the C. elegans genome. In addition, multiple foreign transgenes can be engineered to escape piRNA targeting, allowing successful expression in the germline. Science , this issue p. 587

Aim: To analyze the m6A methylome of osteosarcoma stem cells (OSCs). Materials & methods: Chemoresistant OSCs were enriched by doxorubicin treatment. Expression of m6A-related enzymes was detected ...

Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and shortly denote it as MGAE-DC. A MGAE model is built to learn the drug embeddings by considering not only synergistic combinations but also additive and antagonistic ones as three input channels. The later two channels guide the model to explicitly characterize the features of non-synergistic combinations through an encoder-decoder learning process, and thus the drug embeddings become more discriminative between synergistic and non-synergistic combinations. In addition, an attention mechanism is incorporated to fuse each cell-line’s drug embeddings across various cell lines, and a common drug embedding is extracted to capture the invariant patterns by developing a set of cell-line shared decoders. The generalization performance of our model is further improved with the invariant patterns. With the cell-line specific and common drug embeddings, our method is extended to predict the synergy scores of drug combinations by a neural network module. Experiments on four benchmark datasets demonstrate that MGAE-DC consistently outperforms the state-of-the-art methods. In-depth literature survey is conducted to find that many drug combinations predicted by MGAE-DC are supported by previous experimental studies. The source code and data are available at https://github.com/yushenshashen/MGAE-DC. Drug combination therapy is widely employed for various complex diseases because of its advantages of enhancing the efficiency, overcoming the drug resistance and reducing dose-dependent toxicity relative to monotherapy. To identify novel reliable and efficacious drug combinations for the patients, various methods have been proposed in the past decades. The trial-based methods are based on the clinical trials directly, but may cause patients to receive unnecessary or even harmful treatments. The experimental methods are labour and cost intensive, it is infeasible to test the complete drug combination space due to the combinatorial explosion. Computational methods provide attractive solutions to make the prediction based on the known data in silico, and thus narrow down the searching range of potential combinations. We have developed a novel deep learning method, named MGAE-DC, for predicting the synergistic effects of drug combinations. Our method explicitly characterizes not only synergistic combinations but also non-synergistic ones to obtain discriminative drug embeddings. Our method also learns common features of drug combinations across the cell lines for improved generalization performance. Computational experiments have verified the effectiveness of MGAE-DC.

Traditional drug discovery is very laborious, expensive, and time-consuming, due to the huge combinatorial complexity of the discrete molecular search space. Researchers have turned to machine learning methods for help to tackle this difficult problem. However, most existing methods are either virtual screening on the available database of compounds by protein–ligand affinity prediction, or unconditional molecular generation, which does not take into account the information of the protein target. In this paper, we propose a protein target-oriented de novo drug design method, called AlphaDrug. Our method is able to automatically generate molecular drug candidates in an autoregressive way, and the drug candidates can dock into the given target protein well. To fulfill this goal, we devise a modified transformer network for the joint embedding of protein target and the molecule, and a Monte Carlo tree search (MCTS) algorithm for the conditional molecular generation. In the transformer variant, we impose a hierarchy of skip connections from protein encoder to molecule decoder for efficient feature transfer. The transformer variant computes the probabilities of next atoms based on the protein target and the molecule intermediate. We use the probabilities to guide the look-ahead search by MCTS to enhance or correct the next-atom selection. Moreover, MCTS is also guided by a value function implemented by a docking program, such that the paths with many low docking values are seldom chosen. Experiments on diverse protein targets demonstrate the effectiveness of our methods, indicating that AlphaDrug is a potentially promising solution to target-specific de novo drug design.

Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.

Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein–ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specifically, an SE(3)-invariant expert network is developed to fit the Vina scoring functions and jointly trained with the denoising network, while the domain knowledge is distilled and conveyed from Vina functions to the expert network. An effective guidance is proposed on both continuous atom coordinates and discrete atom types by taking advantages of the gradient of the expert network. Experiments on the benchmark CrossDocked2020 demonstrate the superiority of our method. Additionally, an atom-level explanation of the generated molecules is provided, and the connections with the domain knowledge are established.