https://www.selleckchem.com/products/leptomycinb.html Our results show that Sigmap yields comparable performance on mapping yeast simulated raw signals, and better mapping accuracy on mapping yeast real raw signals with a 4.4× speedup. Moreover, our method performed well on mapping raw signals to genomes of size >100 Mbp and correctly mapped 11.49% more real raw signals of green algae, which leads to a significantly higher F1-score (0.9354 versus 0.8660). Sigmap code is accessible at https//github.com/haowenz/sigmap. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. MoAble is available at https//github.com/dmis-lab/moable. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the hete