Haq / Wilson Frontiers in Computational Chemistry: Volume 8

Volume 8
1. Auflage 2025
ISBN: 979-8-89881-216-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 317 Seiten

Reihe: Frontiers in Computational Chemistry

Frontiers in Computational Chemistry (Volume 8) offers a comprehensive overview of new advances in computational modeling techniques for drug discovery and development. This volume focuses on an overview of computer-aided drug design and therapy, molecular dynamics simulations and quantum mechanical calculations, while presenting some applications in computational chemistry. The edited chapters cover applications in the computational design of therapeutic peptides, protein-ligand interactions, anti-inflammatory drug design targeting the NLRP3 complex, and the study of photophysical processes. Each chapter provides theoretical explanations followed by experimental methods and current research updates.

Autoren/Hrsg.

Haq, Zaheer Ul

Wilson, Angela K.

Fachgebiete

• Naturwissenschaften Chemie Organische Chemie Polymerchemie

Leseproben

Advancements in Computer-Aided Drug Discovery and Development: A Comprehensive Overview

Harshkumar Brahmbhatt1, *, Rahul Trivedi1, Priyanka Soni2, Vishal Soni2

1 Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, Vadodara, Gujarat, India

2 B. R. Nahata College of Pharmacy, Faculty of Pharmacy, Mandsaur University, Mandsaur, Madhya Pradesh, India

Abstract

Computer-aided drug discovery and development (CADD) has emerged as a transformative approach in the pharmaceutical industry, revolutionizing the traditional drug development process. This abstract provides a comprehensive overview of the latest advancements, methodologies, and applications in CADD. The first section outlines the fundamental principles of CADD, emphasizing its integration of computational techniques, algorithms, and databases to expedite the identification of potential drug candidates. Molecular modeling, virtual screening, and quantitative structure-activity relationship (QSAR) analysis are highlighted as primary techniques used to predict ligand-target interactions and optimize drug properties. The second section discusses the role of machine learning (ML) and artificial intelligence (AI) in CADD, showcasing their capability to analyze vast datasets, identify patterns, and predict novel drug-target interactions with unparalleled accuracy. ML algorithms, such as deep learning, have shown promising results in de novo drug design, target identification, and toxicity prediction. In the third section, the application of CADD in various stages of drug discovery and development is explored. From hit identification and lead optimization to pharmacokinetic/pharmacodynamic (PK/PD) modeling and clinical trial design, CADD tools streamline decision-making processes, reduce costs, and accelerate the development timeline. Furthermore, this chapter addresses the challenges and future prospects of CADD. Despite its remarkable achievements, CADD still faces limitations, such as the accurate representation of biological systems and the integration of multi-scale modeling approaches. Additionally, ethical considerations regarding data privacy, intellectual property rights, and regulatory compliance remain pivotal in the widespread adoption of CADD methodologies.

Keywords: CADD, QSAR, Virtual screening.

* Corresponding author Harshkumar Brahmbhatt: Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, Vadodara, Gujarat, India; E-mail: [e-mail]

INTRODUCTION

The landscape of drug discovery and development has experienced a profound transformation with the advent of computer-aided drug discovery and development (CADD). Traditionally, the drug discovery process was arduous, expensive, and time-consuming, often taking over a decade and billions of dollars to bring a new drug to market. However, the integration of computational techniques has revolutionized this paradigm, making the process more efficient, cost-effective, and accurate. This chapter provides a comprehensive overview of the latest advancements, methodologies, and applications in CADD, highlighting its pivotal role in modern pharmaceutical research [1, 2].

Fundamental Principles of CADD

CADD employs a wide array of computational techniques, algorithms, and databases to expedite and enhance the drug discovery process. At its core, CADD aims to predict ligand-target interactions, optimize drug properties, and streamline the decision-making process in drug development. The fundamental principles of CADD can be categorized into several key methodologies: molecular modeling, virtual screening, and quantitative structure-activity relationship (QSAR) analysis [3].

Molecular Modeling

Molecular modeling involves the use of computational techniques to model or mimic the behavior of molecules. It includes methods such as molecular dynamics (MD) simulations, which explore the physical movements of atoms and molecules over time, and quantum mechanics/molecular mechanics (QM/MM) approaches, which provide detailed insights into molecular interactions at quantum levels. These techniques allow researchers to predict the structural and functional properties of drug candidates, facilitating the identification of promising compounds [4].

Virtual Screening

Virtual screening (VS) is a computational process used to search large libraries of compounds to identify those that are most likely to bind to a drug target, usually a protein receptor. There are two main types of virtual screening: ligand-based and structure-based. Ligand-based virtual screening relies on known active compounds to predict the activity of new molecules, while structure-based virtual screening uses the three-dimensional structure of the target protein to identify potential ligands. These methods significantly reduce the number of compounds that need to be tested experimentally, thereby accelerating the drug discovery process [5, 6].

Quantitative Structure-Activity Relationship (QSAR) Analysis

QSAR analysis involves the development of mathematical models to predict the biological activity of compounds based on their chemical structure. By correlating chemical structure with pharmacological activity, QSAR models can predict the efficacy and toxicity of new compounds. This method is invaluable in optimizing drug candidates, ensuring that only the most promising compounds progress through the development pipeline [7].

• Model Development: QSAR models can be developed using various statistical and machine learning techniques. Common approaches include linear regression, decision trees, and neural networks. The choice of model often depends on the complexity of the data and the specific application.
• Descriptors: To correlate chemical structure with biological activity, QSAR analysis uses molecular descriptors, which are numerical values representing different properties of a compound. These can include topological, electronic, steric, and hydrophobic descriptors.
• Validation: A crucial part of developing QSAR models is validation, which ensures that the model can reliably predict the activity of unseen compounds. This is typically done using techniques like cross-validation and external validation with independent test sets.
• Applications: QSAR analysis is not limited to predicting efficacy and toxicity. It can also be employed in environmental chemistry to predict the fate and transport of chemicals, in toxicology to assess potential hazards, and in materials science for designing new materials with specific properties.
• Regulatory Acceptance: Regulatory agencies, such as the FDA and EPA, increasingly recognize the value of QSAR models in risk assessment and regulatory decision-making. However, these models must be rigorously validated to ensure their reliability in predicting real-world outcomes [8, 9].

High-Throughput Screening (HTS)

High-Throughput Screening (HTS) is a powerful technique used in drug discovery that enables the rapid testing of thousands to millions of compounds for their biological activity against specific targets. The method automates the process of compound testing, allowing researchers to quickly identify potential drug candidates from vast chemical libraries. HTS can be employed to identify compounds that interact with multiple targets, leading to a better understanding of complex diseases [10].

Network Pharmacology

Network pharmacology is an innovative approach that examines the complex interactions within biological networks to better understand how drugs affect multiple targets. This methodology integrates systems biology, pharmacology, and bioinformatics to reveal the multifaceted nature of drug actions and their relationships with various biological pathways. Network pharmacology employs graphical models to represent interactions among proteins, genes, and other biomolecules. These networks can illustrate how different components influence each other and how drugs interact with multiple targets. It aids in validating drug targets by providing a broader view of how they fit within the larger biological context [11].

Fragment-Based Drug Design (FBDD)

Fragment-Based Drug Design (FBDD) is a sophisticated method in drug discovery that focuses on identifying small chemical fragments that can bind to specific biological targets. These fragments are typically smaller and less complex than traditional drug-like molecules, allowing for a more systematic approach to finding new drug...