Using Molecular Fingerprints as Descriptors in Toxicity Prediction: A Survey During humansu0027 lifetime, their bodies deal with different chemicals through various sources. Chemical toxicity is a challenging problem that needs rapid and efficient methods for evaluation of environmental chemicals, or medications development. Computer science helps in toxicity prediction through building models from pretested compounds by learning from data. These models raise a flag to avoid trying some combinations for trial in wetlab, which reduces the high cost of clinical trials. A compound chemical structure features are represented by Molecular Fingerprint. This survey searches for answers to Q1 How Fingerprints are dependent on the type of the prediction model? Q2 How valid are Fingerprints in toxicity prediction?. The Toxicology in the 21st Century challenge is the case study on this work. By showing the impact of fingerprints as descriptors on different predictive modeling techniques. The survey shows the independence of fingerprints as descriptors, as they work with different QSAR models. Also a need is noticed to test fingerprints separately without other descriptors in toxicity prediction to test their performance.

TOP: Towards Better Toxicity Prediction by Deep Molecular Representation Learning At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learningbased framework called TOP (the abbreviation of TOxicity Prediction). TOP integrated a serial special data processing methods, a bidirectional gated recurrent unitbased RNN (BiGRU) and a fully connected neural network for endtoend molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five stateoftheart methods.