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Design and Synthesis of Next-Generation Organic Therapeutics Using Molecular Engineering, Computational Drug Discovery, and Precision Medicinal Chemistry
Subject area: Science,Engineering and Technology · Area of research: Chemistry
Abstract
The next generation of organic medicines is one great step forward in modern medicine, in which molecule engineering, in silico drug discovery, organic synthesis, and precision medicinal chemistry principles are merged to overcome the shortcomings of conventional drug development, in which small organic molecules such as heterocyclic compounds, natural product-based molecules, peptide mimics, and specific small-molecule drugs still dominate the discovery, and a plethora of therapies for treatment for difficult diseases, including cancer, infection, neurology, metabolic disease, inflammation diseases, and so on. However, conventional drug discovery is usually associated with longer discovery process, expensive expenditure and experiments, lower predictive efficacy for biological behavior, poor selectivities and resistances and possible adverse effects of drugs so it's necessary to explore rational ways to discover therapeutic molecules, on the contrary the higher complexity of biological target: protein, enzymes, signal pathways and molecular networks demands more intelligent way to generate better molecules, especially for drug-likeness. This framework puts forward an ideal plan, which combines molecule design and engineering, further organic synthesis strategies, computer-aided design of drug discovery (CADD) methods, including molecule docking, QSAR, and AI and its application in prediction of molecule activity, target prediction etc, based on the concept of precision medicine in drug discovery system. The approach from molecule engineering is based on modulation of molecule scaffold, functional group, and molecule-target interactions to tailor the desired characteristics including potency, stability, soluble and selectivity. CADD methodologies (ligand-target interactions, pharmacological properties, drug-likeness and potential toxicity estimation), followed by prediction system which can accelerate the entire process for better lead candidate selection, has enabled rational and rapid design of therapeutically active and effective agents, with increasing utilization of AI (including machine learning and deep learning) as well, to perform the high-throughput virtual screening. Combining these techniques helps us build an all-in-one drug discovery and development framework.
Keywords
Medicinal chemistry, Organic therapeutics, Molecular engineering, Drug discovery, Computational chemistry, Molecular docking
How to cite this paper
@article{1723042,
author = {Dr. K. S. Lamani},
title = {Design and Synthesis of Next-Generation Organic Therapeutics Using Molecular Engineering, Computational Drug Discovery, and Precision Medicinal Chemistry},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {2258-2267},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723042.pdf},
abstract = {The next generation of organic medicines is one great step forward in modern medicine, in which molecule engineering, in silico drug discovery, organic synthesis, and precision medicinal chemistry principles are merged to overcome the shortcomings of conventional drug development, in which small organic molecules such as heterocyclic compounds, natural product-based molecules, peptide mimics, and specific small-molecule drugs still dominate the discovery, and a plethora of therapies for treatment for difficult diseases, including cancer, infection, neurology, metabolic disease, inflammation diseases, and so on. However, conventional drug discovery is usually associated with longer discovery process, expensive expenditure and experiments, lower predictive efficacy for biological behavior, poor selectivities and resistances and possible adverse effects of drugs so it's necessary to explore rational ways to discover therapeutic molecules, on the contrary the higher complexity of biological target: protein, enzymes, signal pathways and molecular networks demands more intelligent way to generate better molecules, especially for drug-likeness. This framework puts forward an ideal plan, which combines molecule design and engineering, further organic synthesis strategies, computer-aided design of drug discovery (CADD) methods, including molecule docking, QSAR, and AI and its application in prediction of molecule activity, target prediction etc, based on the concept of precision medicine in drug discovery system. The approach from molecule engineering is based on modulation of molecule scaffold, functional group, and molecule-target interactions to tailor the desired characteristics including potency, stability, soluble and selectivity. CADD methodologies (ligand-target interactions, pharmacological properties, drug-likeness and potential toxicity estimation), followed by prediction system which can accelerate the entire process for better lead candidate selection, has enabled rational and rapid design of therapeutically active and effective agents, with increasing utilization of AI (including machine learning and deep learning) as well, to perform the high-throughput virtual screening. Combining these techniques helps us build an all-in-one drug discovery and development framework.},
keywords = {Medicinal chemistry, Organic therapeutics, Molecular engineering, Drug discovery, Computational chemistry, Molecular docking},
month = {June},
}