Multi-target dopamine D3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning

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

doi:10.1016/j.ejmech.2019.111975

Cited by 18 publications

(13 citation statements)

References 32 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, dealing with GPCR is challenging because minimal changes in the ligand or in the residue side chain may result in sudden changes of binding affinity, sometimes giving rise to near-discontinuities (activity cliffs), which are difficult to capture computationally. 187 This scenario might be complicated further by the active or inactive state of the GPCR itself. This implies that it is risky to seek correlations based on FEP/TI calculations on GPCRs, particularly when the reference experimental values (in this case mutations) are not obtained under ideal conditions.…”

Section: Relative Binding Energy Estimationmentioning

confidence: 99%

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

2020

Self Cite

View full text Add to dashboard Cite

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation’s role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

show abstract

Section: Relative Binding Energy Estimationmentioning

confidence: 99%

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recombinant systems at least, we witness some amazing activity switches between agonist and "antagonist" properties across different series that require further dynamic considerations (Tan et al, 2020). Destabilization of D3 inactive state(s) and flexibility of the ligands are among the elements that the most recent model available is proposing (Ferraro et al, 2020). Molecular recognition steps, changes in hydration of the ligand binding pocket and ligand dependent receptor configuration changes are also important considerations for D2 and D3 in particular when docking flexible ligands and establishing comparisons (Pal et al, 2019).…”

Section: Dr-ligand Interaction Dynamics and Efficacy Studiesmentioning

confidence: 99%

Dopamine Receptor Subtypes, Physiology and Pharmacology: New Ligands and Concepts in Schizophrenia

Martel¹,

McArthur²

2020

Front. Pharmacol.

170

125

View full text Add to dashboard Cite

Dopamine receptors are widely distributed within the brain where they play critical modulator roles on motor functions, motivation and drive, as well as cognition. The identification of five genes coding for different dopamine receptor subtypes, pharmacologically grouped as D1- (D1 and D5) or D2-like (D2S, D2L, D3, and D4) has allowed the demonstration of differential receptor function in specific neurocircuits. Recent observation on dopamine receptor signaling point at dopamine—glutamate-NMDA neurobiology as the most relevant in schizophrenia and for the development of new therapies. Progress in the chemistry of D1- and D2-like receptor ligands (agonists, antagonists, and partial agonists) has provided more selective compounds possibly able to target the dopamine receptors homo and heterodimers and address different schizophrenia symptoms. Moreover, an extensive evaluation of the functional effect of these agents on dopamine receptor coupling and intracellular signaling highlights important differences that could also result in highly differentiated clinical pharmacology. The review summarizes the recent advances in the field, addressing the relevance of emerging new targets in schizophrenia in particular in relation to the dopamine – glutamate NMDA systems interactions.

show abstract

“…This Research Topic collects selected contributions that deal with both types of modeling approaches, some of which lie at the interface between the two. This "gray box" hybrid approach should not surprise as machine learning and statistical mechanics share several theoretical principles (Ferrarotti et al, 2019;Noé et al, 2019;Agliari et al, 2020;Decherchi and Cavalli, 2020;Ferraro et al, 2020;Tsai et al, 2020) as they both deal with distributions, manifolds, and hence free energies.…”

Section: Editorial On the Research Topic Molecular Dynamics And Machine Learning In Drug Discoverymentioning

confidence: 99%