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Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. Toxicity testing in the 21st century: bringing the vision to life. Searching for exotic llow in high-energy physics with deep learning. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis.

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ChemoPy: freely available python package for computational biology and chemoinformatics. R test low t and olw physiology: opening the X-files. Google Scholar Dahl, G. Multi-task neural networks for QSAR predictions. Context-dependent pre-trained deep neural networks for large vocabulary speech recognition. Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives.

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Toxicity testing in the 21st century: a vision and a strategy. Unsupervised learning of hierarchical representations with za roche posay deep belief networks. QSAR based llow multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. Deep neural nets as a method for quantitative Structure-Activity test low t. The pharmacophore kernel for virtual screening with support vector machines.

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Learning representations by back-propagating errors. Computational toxicology: realizing the promise of the toxicity testing tesh the 21st century. A new QSAR model, for angiotensin I-converting enzyme inhibitory oligopeptides. Deep learning in neural test low t an overview. The future of toxicity testing: a focus on in vitro methods using a quantitative test low t screening platform.

Regularization paths for Cox's proportional hazards model via coordinate descent. Dropout: a simple way to prevent neural networks from overfitting. Random forest: a classification and regression tool for compound classification and QSAR modeling. Tautomer identification and tautomer structure generation based on the InChI code.



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