Drug Designing: Open Access
Open Access

Computational Approaches in Fragment Based Drug Design

Robert Langrea^{* *}Correspondence: Robert Langrea, Department of Chemical Engineering, Massachusetts Institute of Technology, Massachusetts, USA, Email:

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Description

In drug discovery, fragment-based drug design has emerged as an efficient alternative to high throughput screening for the identification of lead compounds. Section based screening and streamlining strategies have made solid progress in many medication disclosure projects with one endorsed drug and a lot more mixtures in clinical preliminaries. The fragment-based drug design begins with the identification of low-molecular-weight compounds or fragments that typically bind to the target of interest with low affinity. After that, the fragments that form high-quality interactions are optimized to produce high-affinity and high-selectivity lead compounds. To find hits, biophysical methods like nuclear magnetic resonance, X-ray crystallography, or surface plasmon resonance must be used because fragments have a low affinity for their targets. These methods are very sensitive, and some of them provide in-depth information about protein fragment interaction that is crucial for optimizing fragment-to-lead. Low throughput, high instrument and experiment costs, and high protein and fragment concentration requirements are just a few of the issues that experimental methods of fragment screening face, despite significant technological advancements in recent years. Computational methods that play a significant role in the design of fragment libraries, the screening of fragments, and the optimization of initial fragment hits were developed in order to address the difficulties posed by experimental screening methods. When used in conjunction with experimental methods, the computational methods of fragment screening and optimization are most effective. The development of fragment-based drug design for important biological targets like protein-protein interactions and membrane proteins like GPCRs has been aided by the combination of experimental techniques with virtual fragment-based screening. An overview of experimental and computational screening methods used in fragment-based drug discovery is provided in this review, with a focus on recent successes in locating potent lead molecules using these methods. In recent decades, fragment-based drug discovery, or Fragment-Based Drug Discovery (FBDD), has gained prominence and interest, particularly in academia. For the purpose of detecting very small molecules, also known as "fragments," that are bound to a specific target, FBDD makes use of the advantages of biophysical and biochemical techniques. Fragment hits are fascinating and simple starting points for the generation of drug leads if selected after thorough evaluation or FBDD for short and that has emerged as an effective method for locating small molecules that bind to numerous therapeutic targets. Despite the fact that there are an increasing number of instances in which FBDD has been utilized for targets that are not proteins, such as RNA, the majority of FBDD campaigns target proteins. The fact that screening a collection of small, simple molecules increases the likelihood of finding a "hit" in comparison to screening larger, more complex molecules is one of the fundamental principles that underpin FBDD. The entire collection of chemical structures that are either drug-like or may be suitable for drug development is referred to as "chemical space" in this context. There may be as many as 1063 distinct drug-like molecules with 30 or fewer heavy atoms, according to estimates. Computational FBDD allows for flexibility in the selection of a fragment library, the generation of protein models, and the prediction of a fragments/compounds docking mode by combining multiple in silico methods. Computational FBDD is superior when designing novel and potential compounds for a specific target because of these characteristics. This review will talk about the most recent developments in computational fragment-based drug design, from well-known methods to novel ideas and technologies. Specifications and benefits of experimental and computational FBDD are specifically contrasted in this review, as are drawbacks and potential outcomes for the future. Multiple organismal functions, including physiological mechanisms and disease, are mediated by cellular proteins. The target diseases or physiological mechanisms can be altered by discovering lead compounds that alter the function of target proteins. The chemical structures of leads can be altered to enhance efficacy, selectivity, and reduce side effects using knowledge of the ligand–receptor interaction. Computer-aided drug design is one rational drug design technology that enables drug discovery based on knowledge of target structures, functional properties, and mechanisms. Structure-based drug designs, in which protein structures are needed, and ligand-based drug design, in which ligand and ligand activities can be used to design compounds that interact with the protein structure, are the two main CADD-based approaches. De novo design, fragment-based drug discovery, docking, and structure-based pharmacophore modelling are all methods in structure-based drug design.