Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning

Mariarosaria Ferraro; Sergio Decherchi; Alessio De Simone; Maurizio Recanatini; Andrea Cavalli; Giovanni Bottegoni

Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning

Mariarosaria Ferraro
, Sergio Decherchi
, Alessio De Simone
, Maurizio Recanatini
, Andrea Cavalli
, Giovanni Bottegoni

Pharmacy

Research output: Other contribution

Abstract

Building on our previously reported studies on the combination of molecular dynamics and machine learning (Decherchi et al., Nature Comm 2015; Decherchi et al., JCIM 2018), we applied a combination of these techniques to identify the structural determinants causing efficacy cliffs at the D3 receptor in a small series of previously reported multi-target compounds.

Original language	English
Publication status	Published - 3 Jun 2019

Keywords

dopamine d3 receptor
FAAH
Molecular Dynamics
Cluster analysis
Multi-target directed ligands
Polypharmacology

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Ferraro, M., Decherchi, S., Simone, A. D., Recanatini, M., Cavalli, A., & Bottegoni, G. (2019, Jun 3). Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning. https://chemrxiv.org/articles/Multi-Target_Dopamine_D3_Receptor_Modulators_Actionable_Knowledge_for_Drug_Design_from_Molecular_Dynamics_and_Machine_Learning/8216060

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title = "Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning",

abstract = "Building on our previously reported studies on the combination of molecular dynamics and machine learning (Decherchi et al., Nature Comm 2015; Decherchi et al., JCIM 2018), we applied a combination of these techniques to identify the structural determinants causing efficacy cliffs at the D3 receptor in a small series of previously reported multi-target compounds.",

keywords = "dopamine d3 receptor, FAAH, Molecular Dynamics, Cluster analysis, Multi-target directed ligands, Polypharmacology",

author = "Mariarosaria Ferraro and Sergio Decherchi and Simone, \{Alessio De\} and Maurizio Recanatini and Andrea Cavalli and Giovanni Bottegoni",

year = "2019",

month = jun,

day = "3",

language = "English",

type = "Other",

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Ferraro, M, Decherchi, S, Simone, AD, Recanatini, M, Cavalli, A & Bottegoni, G 2019, Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning. <https://chemrxiv.org/articles/Multi-Target_Dopamine_D3_Receptor_Modulators_Actionable_Knowledge_for_Drug_Design_from_Molecular_Dynamics_and_Machine_Learning/8216060>

TY - GEN

T1 - Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning

AU - Ferraro, Mariarosaria

AU - Decherchi, Sergio

AU - Simone, Alessio De

AU - Recanatini, Maurizio

AU - Cavalli, Andrea

AU - Bottegoni, Giovanni

PY - 2019/6/3

Y1 - 2019/6/3

N2 - Building on our previously reported studies on the combination of molecular dynamics and machine learning (Decherchi et al., Nature Comm 2015; Decherchi et al., JCIM 2018), we applied a combination of these techniques to identify the structural determinants causing efficacy cliffs at the D3 receptor in a small series of previously reported multi-target compounds.

AB - Building on our previously reported studies on the combination of molecular dynamics and machine learning (Decherchi et al., Nature Comm 2015; Decherchi et al., JCIM 2018), we applied a combination of these techniques to identify the structural determinants causing efficacy cliffs at the D3 receptor in a small series of previously reported multi-target compounds.

KW - dopamine d3 receptor

KW - FAAH

KW - Molecular Dynamics

KW - Cluster analysis

KW - Multi-target directed ligands

KW - Polypharmacology

M3 - Other contribution

ER -

Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning

Abstract

Keywords

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