Application of computational methods for anticancer drug discovery, design, and optimization

Abstract

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Therapeutic value of steroidal alkaloids in cancer: Current trends and future perspectives

“…To avoid the huge costs and labor intensiveness incurred with random screening, different types of in silico approaches, including virtual screening, molecular modeling, molecular docking, QSAR, pharmacophore modeling, highthroughput screening, and cloud computing, are efficient and effective for anticancer drug discovery campaigns using SAs. [150][151][152] This technology has changed the scenario of rational design in the anticancer drug discovery process. Various web-based programs for QSAR [JRC QSAR Model Database, DemQSAR, OCHEM (Online Chemical Modeling Environment), and MC-3DQSAR] are available for the generation of potential leads.…”

Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes

“…Molecular docking 7 is a traditional method used in CADD in which the preferred orientation of a small molecule corresponding to its binding mode is optimised with respect to the target of interest resulting in formation of a stable complex. Docking algorithms can be applied for the search of potential ligands from a library, modelling of binding mode and affinity of candidate or known ligands 8 . In spite of efficiency of docking methods, pharmacophore modelling is used more frequently and generally requires less time 9 , although pharmacophore identification can on occasion arise from a docking study.…”

“…In spite of efficiency of docking methods, pharmacophore modelling is used more frequently and generally requires less time 9 , although pharmacophore identification can on occasion arise from a docking study. It is also more precise than the traditional ligand-based approach 8 . However, protein flexibility is being recognised as of fundamental importance for wider applicability of docking methods and analysis of ligand-induced changes in protein binding sites.…”

“…integrated ligand- and structure-based approaches: (i) interaction-based and (ii) similarity-based docking. The former involves identification of interactions between the protein and target using known physico-chemical data, while the latter focuses on combination of structure-based docking methods with ligand similarity methods 10 that makes VLS much more efficient 8 .…”

Ethnomedicine claim directed in <i>Silico</i> prediction of anticancer activity

“…Besides, their relevance in selecting plants for further investigation in the area of cancer can be enhanced by corresponding the findings with existing scientific literature ( 11 ). Virtual screening methods, which are cost effective, fast and reliable, could be utilized to correlate findings from existing scientific studies and ethnobotanical surveys ( 12 ). In retro, such methodological combination could also help understand the indigenously defined malady in terms of its links to cancer.…”