Following the explosive growth in chemical and biological data, the shift from traditional
methods of drug discovery to computer-aided means has made data mining and machine …

Accurate identification of drug-targets in human body has great significance for designing
novel drugs. Compared with traditional experimental methods, prediction of drug-targets via …

The primary task of machine learning is to extract valuable information from the data that is
generated every day, process it to learn from it, and take useful actions. Original language …

In the pharmaceutical industry it is common to generate many QSAR models from training
sets containing a large number of molecules and a large number of descriptors. The best …

Machine learning algorithms have attained widespread use in assessing the potential
toxicities of pharmaceuticals and industrial chemicals because of their faster speed and …

Accurate diagnostic classification of cancers can greatly help physicians to choose
surveillance and treatment strategies for patients. Following the explosive growth of huge …

Combinatorial therapy is a promising strategy for combating complex diseases by improving
the efficacy and reducing the side effects. To facilitate the identification of drug combinations …

Designing molecules for drugs has been a hot topic for many decades. However, it is hard
and expensive to find a new molecule. Thus, the cost of the final drug is also increased …

A classification and regression tool, JH Friedman's Stochastic Gradient Boosting (SGB), is
applied to predicting a compound's quantitative or categorical biological activity based on a …

Since the last published update in 2014, the SuperPred webserver has been continuously
developed to offer state-of-the-art models for drug classification according to ATC classes …