scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure.

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation.

Motivation: Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods. Results: We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community. Availability and implementation: AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning.

Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates' anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.