4 State-of-the-art modeling techniques in performing docking algorithms and scoring
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Pawan Kumar
Abstract
In drug discovery and molecular biology, molecular docking plays a pivotal role in predicting the binding modes and affinities of small molecules with target proteins. This abstract explores the current state-of-the-art modeling techniques employed in performing docking algorithms and scoring methods. Docking algorithms are essential tools used to predict the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Over the years, various approaches have been developed, ranging from geometric matching to advanced machine learning-based methods. These techniques often integrate molecular mechanics, quantum mechanics, and empirical scoring functions to accurately predict binding poses. Moreover, scoring functions are critical components in evaluating the affinity between the ligand and the receptor. Traditional scoring functions are often based on empirical parameters derived from experimental data. However, recent advancements have witnessed the integration of machine learning models, deep learning architectures, and physics-based potentials to enhance scoring accuracy and reliability. The chapter discusses the significant advancements in docking algorithms, including flexible docking, induced fit docking, and ensemble docking, which better capture the dynamic nature of protein-ligand interactions. Additionally, it highlights the emergence of innovative scoring functions, such as free energy-based scoring and machine learning-driven scoring, which aim to improve the precision of binding affinity predictions. Furthermore, the chapter addresses the challenges and limitations associated with current modeling techniques, including computational complexity, scoring function bias, and the incorporation of protein flexibility. Overall, it provides insights into the cutting-edge methodologies shaping the landscape of molecular docking and scoring, paving the way for more efficient and accurate drug discovery processes.
Abstract
In drug discovery and molecular biology, molecular docking plays a pivotal role in predicting the binding modes and affinities of small molecules with target proteins. This abstract explores the current state-of-the-art modeling techniques employed in performing docking algorithms and scoring methods. Docking algorithms are essential tools used to predict the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Over the years, various approaches have been developed, ranging from geometric matching to advanced machine learning-based methods. These techniques often integrate molecular mechanics, quantum mechanics, and empirical scoring functions to accurately predict binding poses. Moreover, scoring functions are critical components in evaluating the affinity between the ligand and the receptor. Traditional scoring functions are often based on empirical parameters derived from experimental data. However, recent advancements have witnessed the integration of machine learning models, deep learning architectures, and physics-based potentials to enhance scoring accuracy and reliability. The chapter discusses the significant advancements in docking algorithms, including flexible docking, induced fit docking, and ensemble docking, which better capture the dynamic nature of protein-ligand interactions. Additionally, it highlights the emergence of innovative scoring functions, such as free energy-based scoring and machine learning-driven scoring, which aim to improve the precision of binding affinity predictions. Furthermore, the chapter addresses the challenges and limitations associated with current modeling techniques, including computational complexity, scoring function bias, and the incorporation of protein flexibility. Overall, it provides insights into the cutting-edge methodologies shaping the landscape of molecular docking and scoring, paving the way for more efficient and accurate drug discovery processes.
Chapters in this book
- Frontmatter I
- Contents V
- 1 Historical development of computer-aided drug design 1
- 2 Lead-hit-based methods for drug design and ligand identification 23
- 3 Virtual screening tools in ligand and receptor-based drug design 51
- 4 State-of-the-art modeling techniques in performing docking algorithms and scoring 65
- 5 Design of computational chiral compounds for drug discovery and development 81
- 6 Role of integrated bioinformatics in structure-based drug design 91
- 7 Molecular recognizable tools in X-ray crystallography in computer-aided drug design 133
- 8 Design of target hit molecules using molecular dynamic simulations: special key aspects of GROMACS or Role of molecular dynamic simulations in designing a hit molecule for drug discovery 151
- 9 Computational prediction of drug-limited solubility and CYP450-mediated biotransformation 175
- 10 Recent advancement in binding free-energy calculation 211
- 11 Role of structural genomics in drug discovery 243
- 12 Unlocking therapeutic potential: computational approaches for enzyme inhibition discovery 295
- 13 Role of spectroscopy in drug discovery 319
- 14 Computer-aided design of peptidomimetic therapeutics 351
- 15 Developing safer therapeutic agents through toxicity prediction 379
- 16 Identifying prominent molecular targets in the fight against drug resistance 403
- Index 429
Chapters in this book
- Frontmatter I
- Contents V
- 1 Historical development of computer-aided drug design 1
- 2 Lead-hit-based methods for drug design and ligand identification 23
- 3 Virtual screening tools in ligand and receptor-based drug design 51
- 4 State-of-the-art modeling techniques in performing docking algorithms and scoring 65
- 5 Design of computational chiral compounds for drug discovery and development 81
- 6 Role of integrated bioinformatics in structure-based drug design 91
- 7 Molecular recognizable tools in X-ray crystallography in computer-aided drug design 133
- 8 Design of target hit molecules using molecular dynamic simulations: special key aspects of GROMACS or Role of molecular dynamic simulations in designing a hit molecule for drug discovery 151
- 9 Computational prediction of drug-limited solubility and CYP450-mediated biotransformation 175
- 10 Recent advancement in binding free-energy calculation 211
- 11 Role of structural genomics in drug discovery 243
- 12 Unlocking therapeutic potential: computational approaches for enzyme inhibition discovery 295
- 13 Role of spectroscopy in drug discovery 319
- 14 Computer-aided design of peptidomimetic therapeutics 351
- 15 Developing safer therapeutic agents through toxicity prediction 379
- 16 Identifying prominent molecular targets in the fight against drug resistance 403
- Index 429
