Multi-target drugs against particular multiple targets get better protection, resistance profiles and curative influence by cooperative rules of a key beneficial target with resistance behavior and compensatory elements. Computational techniques can assist us in the efforts to design novel drugs (ligands) with a preferred bioactivity outline and alternative bioactive molecules at an early stage. A number of in silico methods have been explored extensively in order to facilitate the investigation of individual target agents and to propose a selective drug. A different, progressively more significant field which is used to predict the bioactivity of chemical compounds is the data mining method. Some of the previously mentioned methods have been investigated for multi-target drug design (MTDD) to find drug leads interact simultaneously with multiple targets. Several cheminformatics methods and structure-based approaches try to extract information from units working cooperatively in a biomolecular system to fulfill their task. To dominate the difficulties of the experimental specification of ligand-target structures, rational methods, namely molecular docking, SAR and QSAR are vital substitutes to obtain knowledge for each structure in atomic insight. These procedures are logically successful for the prediction of binding affinity and have shown promising potential in facilitating MTDD. Here, we review some of the important features of the multi-target therapeutics discoveries using the computational approach, highlighting the SAR, QSAR, docking and pharmacophore methods to discover interactions between drug-target that could be leveraged for curative benefits. A summary of each, followed by examples of its applications in drug design has been provided. Computational efficiency of each method has been represented according to its main strengths and limitations.

No abstract

A Quantitative structure -property relationships analysis has been applied to a series of chemical compounds which were of interest because of their roles in environmental samples. The incorporation neutral solute (log K s ) in anionic micelle (SDS) has been predicted by using molecular structural descriptors for a heterogeneous set of 62 neutral solutes with a range of more than 4log units. Two linear correlating models, Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression methods were used. The stepwise MLR of SPSS software was used for the selection of the variables. After variables selection, MLR and PLS methods used leave-one-out cross-validation for building the regression models. Appropriate models with low standard errors and high correlation coefficients were selected. The results showed that both MLR and PLS methods could model the relationship between solubility and their electronic and thermodynamic descriptors perfectly. The predictive quality of the QSPR models were tested for an external prediction set of 11 compounds randomly chosen from 62 compounds. The squared regression coefficients of prediction for the MLR and PLS regression methods were 0.9679 and 0.9728 respectively.

Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included.

No abstract

Today, the development of new drugs is a challenging task of science. Researchers already applied molecular docking in the drug design field to simulate ligand- receptor interactions. Docking is a term used for computational schemes that attempt to find the “best” matching between two molecules in a complex formed from constituent molecules. It has a wide range of uses and applications in drug discovery. However, some defects still exist; the accuracy and speed of docking calculation is a challenge to explore and these methods can be enhanced as a solution to docking problem. The molecular docking problem can be defined as follows: Given the atomic coordinates of two molecules, predict their “correct” bound association. The chapter discusses common challenges critical aspects of docking method such as ligand- and receptor- conformation, flexibility and cavity detection, etc. It emphasis to the challenges and inadequacies with the theories behind as well as the examples.

No abstract

Cyclodextrin (CD) is a subset of the macrocyclic structural class, which is an important class of small organic agents that are useful functional excipients. They have wide range application possibilities in different fields of sciences such as material preparation, medicine, analytical chemistry, and separation processes. They are used widely in pharmaceutical formulations and drug delivery for increasing the water solubility of low soluble drugs and drug candidates. Due to the ring structure, they behave differently than smaller molecules and may be capable of hitting new classes of targets. A macrocyclic molecule presents varied functionality and stereochemical complexity in a pre-organized conformation of the ring structure. This can result in high selectivity and affinity for protein targets while conserving enough bioavailability to arrive at intracellular locations. Regardless of these valuable features, and the verified success of several marketed macrocycle drugs isolated from natural compounds, this class has been little explored in drug development. This study describes some of the key features of the CDs therapeutic discovery. Also, the application of computational chemistry approaches such as QSAR/QSPR, molecular docking, and molecular/quantum mechanics for modeling of CD-drug system is reviewed briefly. K E Y W O R D Sbioavailability, computational, cyclodextrin, drug design, drug discovery, macrocyclic, therapeutic