Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

UNASSIGNED: Non-coding RNAs control cell functioning through affecting gene expression and translation and their dysregulation is associated with altered cell homeostasis and diseases, including cancer. Nutraceuticals with anti-cancer therapeutic potential have been shown to modulate non-coding RNAs expression that could impact on the expression of genes involved in the malignant phenotype.
UNASSIGNED: Here, we report on the microarray profiling of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) and on the associated biochemical pathways and functional processes potentially modulated in OVCAR-3 ovarian cancer cells exposed for 24 h to Resveratrol (RV), a nutraceutical that has been shown to inhibit carcinogenesis and cancer progression in a variety of human and animal models, both in vitro and in vivo. Diana tools and Gene Ontology (GO) pathway analyses along with Pubmed literature search were employed to identify the cellular processes possibly affected by the dysregulated miRNAs and lncRNAs.
UNASSIGNED: The present data consistently support the contention that RV could exert anti-neoplastic activity via non-coding RNAs epigenetic modulation of the pathways governing cell homeostasis, cell proliferation, cell death and cell motility.