1 Historical development of computer-aided drug design
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Arshdeep Singh
Abstract
The 1970s witnessed the beginning of the historical development of computer- aided drug design (CADD), which revolutionised drug discovery by utilising computational techniques to increase accuracy and efficiency. Molecular modelling and quantitative structure-activity relationship (QSAR) models, which connected chemical structures with biological activities to forecast medication efficacy, were the main focuses of early CADD initiatives. Developments in molecular docking and dynamics simulations were crucial in the 1980s and 1990s. While molecular dynamics simulations looked at the stability and interactions of drug compounds over time, molecular docking predicted how drug candidates would bind to target proteins. Drug development was further expedited by the late 20th century combination of combinatorial chemistry and high-throughput screening, which combined CADD with experimental methods. With the introduction of artificial intelligence (AI) and machine learning (ML) into CADD, a major advancement has occurred in the twenty-first century. These tools aid in the search for new treatment candidates by quickly and accurately analysing large datasets, spotting patterns, and making predictions about the future. All things considered, CADD’s development is a reflection of ongoing advances in computer technology, which makes it an essential part of contemporary pharmaceutical research. It keeps changing, taking advantage of fresh chances in medication discovery and design as well as new obstacles.
Abstract
The 1970s witnessed the beginning of the historical development of computer- aided drug design (CADD), which revolutionised drug discovery by utilising computational techniques to increase accuracy and efficiency. Molecular modelling and quantitative structure-activity relationship (QSAR) models, which connected chemical structures with biological activities to forecast medication efficacy, were the main focuses of early CADD initiatives. Developments in molecular docking and dynamics simulations were crucial in the 1980s and 1990s. While molecular dynamics simulations looked at the stability and interactions of drug compounds over time, molecular docking predicted how drug candidates would bind to target proteins. Drug development was further expedited by the late 20th century combination of combinatorial chemistry and high-throughput screening, which combined CADD with experimental methods. With the introduction of artificial intelligence (AI) and machine learning (ML) into CADD, a major advancement has occurred in the twenty-first century. These tools aid in the search for new treatment candidates by quickly and accurately analysing large datasets, spotting patterns, and making predictions about the future. All things considered, CADD’s development is a reflection of ongoing advances in computer technology, which makes it an essential part of contemporary pharmaceutical research. It keeps changing, taking advantage of fresh chances in medication discovery and design as well as new obstacles.
Kapitel in diesem Buch
- 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
Kapitel in diesem Buch
- 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
