Chemoinformatics in Drug Designing
Mushtaque S. Shaikh, Anand S. Chintakrindi · Apple Academic Press eBooks · 2024
The drug design is a time-consuming process that involves a series of steps from target identification, target validation, lead identification, and lead optimization. The lead identification and optimization steps are iterative that require the synthesis and evaluation of millions of compounds. However, the use of chemoinformatics in drug design has hastened this process. Chemoinformatics is the study of processing enormous quantities of data in chemical space to retrieve meaningful chemical information. Briefly, chemoinformatics encompasses techniques for representing 2D and 3D chemical structures in a computer language to build molecular databases, calculation, and enumeration of physicochemical properties of large databases of molecules, and ligand-based virtual screening techniques based on calculated molecular properties. The application of chemoinformatics in drug design allows early prediction of ADMET properties and drug-likeness screening (virtual screening) of a huge set of compounds prior to their synthesis thus reducing the time required for the lead optimization process and eliminating the risk of failure of compounds at later stages of drug development. It also supports the high-throughput screening techniques and combinatorial chemistry used in drug design by extracting scientifically relevant information from the huge data produced by these two techniques. This chapter focuses on the cheminformatics techniques and their use in drug design. Beginning with a brief introduction to chemoinformatics, various aspects of chemical structure representation in 2D and 3D, the different ADMET properties of compounds used in chemoinformatics, and the several virtual screening techniques applied in chemoinformatics will be discussed. A brief overview of different software used in chemoinformatics is also discussed. This chapter concludes with new advances in chemoinformatics involving the use of Artificial intelligence and Machine learning.