Abstract
Modern drug design relies on a detailed understanding of the molecular recognition process by which biological partners such as a protein and a drug interact and bind to each other. Accounting for the correct structural rearrangements accompanying this process by means of generally applicable and high-throughput in silico methods such as molecular docking represents a great challenge. In this review, we summarize the recent advances in the modeling of molecular recognition accounting for receptor flexibility. First, we outline the theoretical background behind molecular recognition events. Next, we introduce the reader to molecular docking, focusing on some of the current methodologies to treat receptor flexibility and highlighting some of the most successful approaches developed in the last years. Finally, we describe approaches based on molecular dynamics simulations and machine learning algorithms to simulate molecular recognition events. Along the review, we discuss the major strengths and pitfalls of current methodologies, mentioning possible future developments in the field.
| Original language | English |
|---|---|
| Title of host publication | Virtual Screening and Drug Docking |
| Editors | Julio Caballero |
| Publisher | Academic Press |
| Pages | 43-97 |
| Number of pages | 55 |
| ISBN (Print) | 9780323985956 |
| DOIs | |
| Publication status | Published - Jan 2022 |
Publication series
| Name | Annual Reports in Medicinal Chemistry |
|---|---|
| Volume | 59 |
| ISSN (Print) | 0065-7743 |
Bibliographical note
Publisher Copyright:© 2022 Elsevier Inc.
Keywords
- Ligand-protein binding
- Machine learning
- Molecular docking
- Molecular dynamics
- Molecular recognition
- Monte Carlo
- Normal modes
- Protein flexibility