Identifying new lead molecules to treat cancer requires more than a decade of dedicated effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is generally validated by in vitro cellular experiments. Therefore, accurate prediction of cancer drug response is a critical and challenging task for anti-cancer drugs design and precision medicine. With the development of pharmacogenomics, the combination of efficient drug feature extraction methods and omics data has made it possible to use computational models to assist in drug response prediction. In this study, we propose DeepTTA, a novel end-to-end deep learning model that utilizes transformer for drug representation learning and a multilayer neural network for transcriptomic data prediction of the anti-cancer drug responses. Specifically, DeepTTA uses transcriptomic gene expression data and chemical substructures of drugs for drug response prediction. Compared to existing methods, DeepTTA achieved higher performance in terms of root mean square error, Pearson correlation coefficient and Spearman’s rank correlation coefficient on multiple test sets. Moreover, we discovered that anti-cancer drugs bortezomib and dactinomycin provide a potential therapeutic option with multiple clinical indications. With its excellent performance, DeepTTA is expected to be an effective method in cancer drug design.

Highlights d We developed a novel DL-based method for drug repositioning (HeTDR) d HeTDR succeeds in fusing networks topology information and text mining information d HeTDR obtains high accuracy, excessing most state-of-theart models d HeTDR could represent an algorithm integrating multiple sources of information

Potential miRNA–disease associations (MDA) play an important role in the discovery of complex human disease etiology. Therefore, MDA prediction is an attractive research topic in the field of biomedical machine learning. Recently, several models have been proposed for this task, but their performance limited by over-reliance on relevant network information with noisy graph structure connections. However, the application of self-supervised graph structure learning to MDA tasks remains unexplored. Our study is the first to use multi-view self-supervised contrastive learning (MSGCL) for MDA prediction. Specifically, we generated a learner view without association labels of miRNAs and diseases as input, and utilized the known association network to generate an anchor view that provides guiding signals for the learner view. The graph structure was optimized by designing a contrastive loss to maximize the consistency between the anchor and learner views. Our model is similar to a pre-trained model that continuously optimizes upstream tasks for high-quality association graph topology, thereby enhancing the latent representation of association predictions. The experimental results show that our proposed method outperforms state-of-the-art methods by 2.79$\%$ and 3.20$\%$ in area under the receiver operating characteristic curve (AUC) and area under the precision/recall curve (AUPR), respectively.

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Using genetic algorithm, we propose a method to retrieve the alignment distribution of transiently aligned CO2 molecules from the high-order harmonic generation (HHG) spectra. The retrieval method is based on the quantitative rescattering (QRS) model where averaged photorecombination transition dipole can be factored out from the parallel (or perpendicular) harmonic spectra after the propagation of the harmonic fields in the gas medium. We examine how the retrieved alignment distributions are affected by uncertainty in alignment-dependent ionization probability and on multiple orbital contribution to the HHG. We further confirm that alignment distribution is more accurately retrieved by using the minima in the HHG spectra driven by a long-wavelength laser. In addition, we show that earlier experimental data on the ratios between the perpendicular and the parallel HHG components of aligned CO2 molecules are in better agreement with the QRS model if the macroscopic propagation and multiple orbital interference are included in the theoretical calculation.