Xiang Cheng

TL;DR: A multi‐label classifier, called iATC‐mISF, was developed by incorporating the information of chemical‐chemical interaction, the informationOf the structural similarity, and theInformation of the fingerprintal similarity, which showed that the proposed predictor achieved remarkably higher prediction quality than its cohorts for the same purpose.

TL;DR: The neighborhood cleaning rule and synthetic minority over-sampling technique are adopted and a new predictor called iDrug-Target was developed that contains four sub-predictors, specialized for identifying the interactions of drug compounds with GPCRs, ion channels, enzymes, and NR (nuclear receptors), respectively.

TL;DR: A new predictor called ‘pLoc‐mAnimal’ is proposed, which is superior to iLoc‐Animal, and when tested by the most rigorous cross‐validation on the same high‐quality benchmark dataset, the absolute true success rate achieved by the new predictor is 37% higher and the absolute false rate is four times lower in comparison with the state‐of‐the‐art predictor.

TL;DR: It has been demonstrated by the rigorous cross-validation and from five different measuring angles that iATC-mHyb is remarkably superior to the best existing predictor in identifying the ATC classes for drug compounds.

TL;DR: The long‐range and short‐range contact in protein were used to derive extended version of the pseudo amino acid composition based on sliding window method, capable of predicting the protein folding rates just from the amino acid sequence without the aid of any structural class information.