Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.

‘Drug‐likeness’, a qualitative property of chemicals assigned by experts committee vote, is widely integrated into the early stages of lead and drug discovery. Its conceptual evolution paralleled work related to Pfizer's ‘rule of five’ and lead‐likeness, and is placed within this framework. The discrimination between ‘drugs’ (represented by a collection of pharmaceutically relevant small molecules, some of which are marketed drugs) and ‘nondrugs’ (typically, chemical reagents) is possible using a wide variety of statistical tools and chemical descriptor systems. Here we summarize 18 papers focused on drug‐likeness, and provide a comprehensive overview of progress in the field. Tools that estimate drug‐likeness are valuable in the early stages of lead discovery, and can be used to filter out compounds with undesirable properties from screening libraries and to prioritize hits from primary screens. As the goal is, most often, to develop orally available drugs, it is also useful to optimize drug‐like pharmacokinetic properties. We examine tools that evaluate drug‐likeness and some of their shortcomings, challenges facing these tools, and address the following issues: What is the definition of drug‐likeness and how can it be utilized to reduce attrition rate in drug discovery? How difficult is it to distinguish drugs from nondrugs? Are nondrug datasets reliable? Can we estimate oral drug‐likeness? We discuss a drug‐like filter and recent advances in the prediction of oral drug‐likeness. The heuristic aspect of drug‐likeness is also addressed. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 760–781 DOI: 10.1002/wcms.52 This article is categorized under: Computer and Information Science > Chemoinformatics

The use of splice‐switching antisense therapy is highly promising, with a wealth of pre‐clinical data and numerous clinical trials ongoing. Nevertheless, its potential to treat a variety of disorders has yet to be realized. The main obstacle impeding the clinical translation of this approach is the relatively poor delivery of antisense oligonucleotides to target tissues after systemic delivery. We are a group of researchers closely involved in the development of these therapies and would like to communicate our discussions concerning the validity of standard methodologies currently used in their pre‐clinical development, the gaps in current knowledge and the pertinent challenges facing the field. We therefore make recommendations in order to focus future research efforts and facilitate a wider application of therapeutic antisense oligonucleotides.

The proposed anti-inflammatory model can be utilized for the virtual screening of large chemical databases and for indexing natural products for potential anti-inflammatory activity.