Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Schultz, Katherine J.; Colby, Sean; Yesiltepe, Yasemin; Nuñez, Jamie; McGrady, Monee; Renslow, Ryan

doi:10.1039/d0cp03620j

Cited by 10 publications

(9 citation statements)

References 145 publications

(79 reference statements)

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“…Additionally, as mentioned, this module could be used to help assess other experimentally measurable or predicted properties, such as toxicity, ligand affinity, and blood–brain barrier permeability. One example would be expanding the information used to predict ligand-based affinity for specific receptors, which are often based on a small set of input parameters …”

Section: Discussionmentioning

confidence: 99%

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Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Nuñez

McGrady

Yesiltepe

et al. 2020

J. Chem. Inf. Model.

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Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable Python module, chespa, built to easily assess different chemical space definitions through clustering of compounds in these spaces and visualizing trends of these clusters. To demonstrate this, chespa currently streamlines prediction of various molecular descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test six different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, that is, the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA’s nontargeted analysis collaborative trial, where blinded samples had been analyzed previously, providing 1398 data points. Improved understanding of observability would offer many advantages in small-molecule identification, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property nontrivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

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Section: Discussionmentioning

confidence: 99%

“…One example would be expanding the information used to predict ligand-based affinity for specific receptors, which are often based on a small set of input parameters. 46…”

Section: ■ Conclusionmentioning

confidence: 99%

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Nuñez

McGrady

Yesiltepe

et al. 2020

J. Chem. Inf. Model.

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“…The advancements in AI allowed for the development of technological-oriented methodologies. These in silico analyses allowed for the assessment of drug design [ 17 , 87 , 88 , 89 ], repositioning [ 90 , 91 ], and pharmacological combinations [ 92 , 93 ]. Additionally, contributed as tools used in genetic [ 94 , 95 ] and immune-targeted [ 7 , 96 ] therapies.…”

Section: Main Applications Of Ai In Ad Researchmentioning

confidence: 99%

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

et al. 2022

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Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

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“…The Renslow group is amongst the few to have already incorporated AI into neurodegenerative drug discovery [ 65 ]. These investigators described twelve unique compounds that possibly antagonize the phencyclidine (PCP)-binding domain of the N -methyl- d -aspartate (NMDA) receptor.…”

Section: Innovative Pharmacotherapies For Treating Iron-loading: the New World Order Of Implementing Ai In Drug Design And Developmentmentioning

confidence: 99%

Calcium channels and iron metabolism: A redox catastrophe in Parkinson's disease and an innovative path to novel therapies?

Boag

Mellick

et al. 2021

Redox Biology

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Autonomously spiking dopaminergic neurons of the substantia nigra pars compacta (SNpc) are exquisitely specialized and suffer toxic iron-loading in Parkinson's disease (PD). However, the molecular mechanism involved remains unclear and critical to decipher for designing new PD therapeutics. The long-lasting (L-type) Ca V 1.3 voltage-gated calcium channel is expressed at high levels amongst nigral neurons of the SNpc, and due to its role in calcium and iron influx, could play a role in the pathogenesis of PD. Neuronal iron uptake via this route could be unregulated under the pathological setting of PD and potentiate cellular stress due to its redox activity. This Commentary will focus on the role of the Ca V 1.3 channels in calcium and iron uptake in the context of pharmacological targeting. Prospectively, the audacious use of artificial intelligence to design innovative Ca V 1.3 channel inhibitors could lead to breakthrough pharmaceuticals that attenuate calcium and iron entry to ameliorate PD pathology.

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Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Abstract: In this study, we assess the application of a generative model to the NMDAR and provide source code for a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds.

Cited by 10 publications

References 145 publications

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Calcium channels and iron metabolism: A redox catastrophe in Parkinson's disease and an innovative path to novel therapies?

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