"Toxicity refers to the interaction between chemical molecules that leads to adverse effects in biological systems, and mutagenicity is one of its most important endpoints. Prediction of chemical mutagenicity is essential to ensuring the safety of drugs, foods, etc. In silico modeling of chemical mutagenicity, as a replacement of in-vivo bioassays, is increasingly encouraged, due to its efficiency, effectiveness, lower cost and less reliance on animal tests. The quality of a good molecular representation is usually the key to building an accurate and robust in silico model, in that each representation provides a different way for the machine to look at the molecular structure. While most molecular descriptors were introduced based on the physio-chemical and biological activities of chemical molecules, in this study, we propose a new topological representation for chemical molecules, the combinatorial topological fingerprints (CTFs) based on persistent homology, knowing that persistent homology is a suitable tool to extract global topological information from a discrete sample of points. The combination of the proposed CTFs and machine learning algorithms could give rise to efficient and powerful in silico models for mutagenic toxicity prediction. Experimental results on a developmental toxicity dataset have also shown the predictive power of the proposed CTFs and its competitive advantages of characterizing and representing chemical molecules over existing fingerprints."--Page ii.
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