Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands

Pérez-Castillo, Yunierkis; Helguera, Aliuska Morales; Cordeiro, M. Natália D. S.; Tejera, Eduardo; Paz‐y‐Miño, César; Sánchez-Rodríguez, Aminael; Borges, Fernanda; Cruz-Monteagudo, Maykel

doi:10.2174/1570159x15666170109143757

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…In recent years, it has become increasingly clear that virtual high-throughput screening (vHTS), which involves computationally screening libraries of molecules to discover hits, can yield hit rates that are significantly higher than random screens. − The computational methods deployed in vHTS vary widely, owing to an array of approximations, implementation choices, and availability of data. Some methods rely on similarity to known active molecules (ligand-based), while others use information about the protein target (structure-based).…”

Section: Introductionmentioning

confidence: 99%

Rigorous Free Energy Simulations in Virtual Screening

Cournia

Allen

Beuming³

et al. 2020

J. Chem. Inf. Model.

183

184

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Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

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Section: Introductionmentioning

confidence: 99%

Rigorous Free Energy Simulations in Virtual Screening

Cournia

Allen

Beuming³

et al. 2020

J. Chem. Inf. Model.

183

184

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show abstract

“…In other research with a main focus on A 2A R and MAO-B, Perez-Castillo et al [ 21 ] propose a different approach. Their methodology includes docking of a set of molecules (including known dual-target ligands and decoys) to both receptors using six different scoring functions and subsequent rescoring of the docking poses.…”

Section: Introductionmentioning

confidence: 99%

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

Alov

Stoimenov

Lessigiarska

et al. 2022

IJMS

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The conventional treatment of neurodegenerative diseases (NDDs) is based on the “one molecule—one target” paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as “multi-target-directed ligands” (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.

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“…A final score was obtained by an average ranking of each protein across 14 models, with the final top-ranking targets predicted to be the most likely protein targets of the input drug list. Comparable consensus-oriented strategies are often applied in virtual screening to exploit the strengths of multiple models ( Gaudelet et al, 2021 ; James et al, 2020 ) and achieve improved performance ( Palacio-Rodríguez et al, 2019 ; Perez-Castillo et al, 2017 ). DeepPurpose models showed promising performance in various testing scenarios, and we refer to the original publication for further details.…”

Section: Methodsmentioning

confidence: 99%

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Timmons

Anighoro²,

Brogan

et al. 2022

eLife

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Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

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Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands

Cited by 11 publications

References 39 publications

Rigorous Free Energy Simulations in Virtual Screening

Rigorous Free Energy Simulations in Virtual Screening

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

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