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As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.
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Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.