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In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecu...

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Autores principales: de Souza Neto, Lauro Ribeiro, Moreira-Filho, José Teófilo, Neves, Bruno Junior, Maidana, Rocío Lucía Beatriz Riveros, Guimarães, Ana Carolina Ramos, Furnham, Nicholas, Andrade, Carolina Horta, Silva, Floriano Paes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040036/
https://www.ncbi.nlm.nih.gov/pubmed/32133344
http://dx.doi.org/10.3389/fchem.2020.00093
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author de Souza Neto, Lauro Ribeiro
Moreira-Filho, José Teófilo
Neves, Bruno Junior
Maidana, Rocío Lucía Beatriz Riveros
Guimarães, Ana Carolina Ramos
Furnham, Nicholas
Andrade, Carolina Horta
Silva, Floriano Paes
author_facet de Souza Neto, Lauro Ribeiro
Moreira-Filho, José Teófilo
Neves, Bruno Junior
Maidana, Rocío Lucía Beatriz Riveros
Guimarães, Ana Carolina Ramos
Furnham, Nicholas
Andrade, Carolina Horta
Silva, Floriano Paes
author_sort de Souza Neto, Lauro Ribeiro
collection PubMed
description Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET—absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.
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spelling pubmed-70400362020-03-04 In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery de Souza Neto, Lauro Ribeiro Moreira-Filho, José Teófilo Neves, Bruno Junior Maidana, Rocío Lucía Beatriz Riveros Guimarães, Ana Carolina Ramos Furnham, Nicholas Andrade, Carolina Horta Silva, Floriano Paes Front Chem Chemistry Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET—absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds. Frontiers Media S.A. 2020-02-18 /pmc/articles/PMC7040036/ /pubmed/32133344 http://dx.doi.org/10.3389/fchem.2020.00093 Text en Copyright © 2020 de Souza Neto, Moreira-Filho, Neves, Maidana, Guimarães, Furnham, Andrade and Silva. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
de Souza Neto, Lauro Ribeiro
Moreira-Filho, José Teófilo
Neves, Bruno Junior
Maidana, Rocío Lucía Beatriz Riveros
Guimarães, Ana Carolina Ramos
Furnham, Nicholas
Andrade, Carolina Horta
Silva, Floriano Paes
In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
title In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
title_full In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
title_fullStr In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
title_full_unstemmed In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
title_short In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
title_sort in silico strategies to support fragment-to-lead optimization in drug discovery
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040036/
https://www.ncbi.nlm.nih.gov/pubmed/32133344
http://dx.doi.org/10.3389/fchem.2020.00093
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