A Guide to In Silico Drug Design

Abstract

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Investigating the Chemical Reactivity of Kahalalides: A Promising Source of Therapeutic Peptides from Marine Natural Products Using Conceptual Density Functional Theory

“…In this way, we were able to use homology modeling to estimate the Bioactivity Scores for a wide variety of pharmacological targets, such as GPCR ligands, kinase inhibitors, ion channel modulators, enzymes, and nuclear receptors. [28][29][30]…”

“…Finally, we used the web‐based Molinspiration software by Molinspiration Cheminformatics (Slovensky Grob, Slovak Republic) to analyze the chemicals in question using Simplified Molecular Input Line Entry Specification (SMILES) notation. In this way, we were able to use homology modeling to estimate the Bioactivity Scores for a wide variety of pharmacological targets, such as GPCR ligands, kinase inhibitors, ion channel modulators, enzymes, and nuclear receptors [28–30] …”

“…Overall, our study is expected to contribute to the discovery of new therapeutic peptides from natural sources by providing insights into their chemical reactivity and potential biological targets. [28][29][30][31][32][33]…”

“…These scores can be used to predict the peptides’ corresponding biological targets, which is an essential step in drug development. Overall, our study is expected to contribute to the discovery of new therapeutic peptides from natural sources by providing insights into their chemical reactivity and potential biological targets [28–33] …”

In Silico Structural Modeling and Binding Site Analysis of Cerebroside Sulfotransferase (CST): A Therapeutic Target for Developing Substrate Reduction Therapy for Metachromatic Leukodystrophy

“…With computational advancement, a huge pull of data is available related to natural as well as synthetic bioactive molecules, which gives strength to the preliminary-stage drug discovery process with in silico- based drug screening. − The three-dimensional (3D) structure of a protein provides important information about protein molecular arrangement, which becomes the basis for inhibitor development. , So far, the 3D structure of CST through an experimental crystallization technique has not been generated. This information gap hinders the development of inhibitors for establishing a therapeutic potential of SRT in MLD.…”