Abstract
Target-based drug discovery tools have been used with success in the pharmaceutical industry. They have become the fundamental methodologies for discovering new drugs in recent years, with two main advantages over the traditional methodologies: increased speed and greater economic efficiency. Improved computational capacities and new software packages have allowed the diversification and strengthening of these procedures. This chapter describes the main concepts related to target-based drug discovery, including two key steps—target and binding site identification—as well as the main features and limitations of the most common target-based methodologies: de novo drug discovery, molecular docking, and molecular dynamics.
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Abbreviations
- 3D:
-
Tridimensional
- CADD:
-
Computer-aided drug design
- COCONUT:
-
Collection of Open Natural Products
- Cryo-EM:
-
Cryogenic electron microscopy
- DL:
-
Deep Learning
- DNDD:
-
De novo drug design
- DRL:
-
Deep Reinforcement Learning
- HIV:
-
Human immunodeficiency virus
- Ki:
-
Inhibition constant
- LBDD:
-
Ligand-based drug design
- MC:
-
Monte Carlo
- MD:
-
Molecular dynamics
- MM:
-
Molecular mechanics
- PDB:
-
Protein databank
- QM:
-
Quantum mechanics
- ReLeaSE:
-
Reinforcement Learning for Structural Evolution
- RL:
-
Reinforcement Learning
- SBDD:
-
Structure-based drug design
- SBVS:
-
Structure-based virtual screening
- vdW:
-
Van der Waals
- VS:
-
Virtual screening
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We thank the CNPq and Capes for financial Support, Grant Numbers 309648/2019-0 and 431254/2018-4.
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Herrera-Acevedo, C., Perdomo-Madrigal, C., de Sousa Luis, J.A., Scotti, L., Scotti, M.T. (2022). Drug Discovery Paradigms: Target-Based Drug Discovery. In: Scotti, M.T., Bellera, C.L. (eds) Drug Target Selection and Validation. Computer-Aided Drug Discovery and Design, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-95895-4_1
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