In silico three-dimensional pharmacophores for aiding the discovery of the Pfmrk (Plasmodium cyclin-dependent protein kinases) specific inhibitors for the therapeutic treatment of malaria

Proposition of In silico Pharmacophore Models for Malaria: A Review

In the field of medicinal chemistry, the concept of pharmacophore refers to the specific region of a molecule that possesses essential structural and chemical characteristics for binding to a receptor and eliciting biological activity. Understanding the pharmacophore is crucial for drug research and development, as it allows the design of new drugs. Malaria, a widespread disease, is commonly treated with chloroquine and artemisinin, but the emergence of parasite resistance limits their effectiveness. This study aims to explore computer simulations to discover a specific pharmacophore for Malaria, providing new alternatives for its treatment. A literature review was conducted, encompassing articles proposing a pharmacophore for Malaria, gathered from the "Web of Science" database, with a focus on recent publications to ensure up-to-date analysis. The selected articles employed diverse methods, including ligand-based and structurebased approaches, integrating molecular structure and biological activity data to yield comprehensive analyses. Affinity evaluation between the proposed pharmacophore and the target receptor involved calculating free energy to quantify their interaction. Multiple linear regression was commonly utilized, though it is sensitive to multicollinearity issues. Another recurrent methodology was the use of the Schrödinger package, employing tools such as the Phase module and the OPLS force field for interaction analysis. Pharmacophore model proposition allows threedimensional representations guiding the synthesis and design of new biologically active compounds, offering a promising avenue for discovering therapeutic agents to combat Malaria.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.