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Drug Discovery Paradigms: Target-Based Drug Discovery

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Drug Target Selection and Validation

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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Acknowledgments

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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