In Silico Classification Models for Anticancer Drugs
Pp. 115-134 (20)
R. Dutt and A.K. Madan
In spite of extensive research in the understanding of neoplastic diseases, the
success rate for oncology drugs continues to be very low. Accordingly, a major
challenge before the scientific community is to design new chemical entities that will be
highly selective for cancer cells so as to minimize side effects. An urgent need for
prompt adaptation and systematic utilization of In silico approaches in drug
design/research has received wide acceptance due to its potential in accelerating drug
discovery process with improved efficiency. Amongst In silico approaches,
classification models play a prominent role in prediction of the biological properties of
newly designed compounds before their synthesis and to prevent non-optimal use of
resources. These models can be derived from either in vitro or in vivo assay data and
can be subsequently utilized for better understanding/extrapolation of data in terms of
desired biological activity. Successful validation of the said predictive model(s) leads to
swift cycle times, minimization of animal sacrifice and an early indication of drug
attrition/failure amalgamated with reduced cost. Though correlation models far
outnumber classification models for development of various therapeutic agents but the
significance of classification models for development of anti-cancer agents cannot be
underestimated. Various machine learning techniques employed for development of
classification models for anti- cancer activity have been briefly reviewed in this chapter.
Anticancer agents, classification modeling, classification techniques,
decision tree, molecular descriptors, moving average analysis, random forest.
Faculty of Pharmaceutical Sciences, Pt. B.D. Sharma University of Health Sciences, Rohtak-124001, India.