Reverse molecular docking and deep-learning to make predictions of receptor activity for neurotoxicology

McCarthy, Michael J.; Chushak, Yaroslav; Gearhart, Jeffery M.

doi:10.1016/j.comtox.2022.100238

Cited by 3 publications

(4 citation statements)

References 53 publications

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“…Furthermore, they could also compare the pharmacological binding properties of 5-HT1B/5-HT2B and human 5-HT1B/rodent 5-HT1B. It is noted that recently, several experimental and computational studies have greatly expanded and enhanced our knowledge and information about these receptors and thus potentially provided new opportunities to better understand their ligand binding properties [ 50 , 51 , 52 ]. The latest crystallographic data on dopamine receptors was published recently, which reports cryogenic electron microscopy structures of all five subtypes of human dopamine receptors (D1–D5) in complex with G protein [ 53 ].…”

Section: Computational Modelingmentioning

confidence: 99%

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Karabulut

Kaur²,

Gauld

2023

Molecules

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Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

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Section: Computational Modelingmentioning

confidence: 99%

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Karabulut

Kaur²,

Gauld

2023

Molecules

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“…The pipeline was showcased to identify toluene both as a GABA B receptor agonist and a toxin with potential neurological activity (McCarthy et al, 2022;Win-Shwe & Fujimaki, 2010).…”

Section: Clinical Responsesmentioning

confidence: 99%

“…The expanded parameter analysis allowed the team to predict the in vivo activity of a drug from in vitro parameters (Benredjem et al, 2019). A protocol that combines reverse molecular docking to obtain interactions between key residues and 2D‐convolutional neural networks to extract interaction features has also been proposed to rapidly assess the neurotoxicity of molecules (McCarthy et al, 2022). The pipeline was showcased to identify toluene both as a GABA B receptor agonist and a toxin with potential neurological activity (McCarthy et al, 2022; Win‐Shwe & Fujimaki, 2010).…”

Section: At What Stages Can Ai Be Employed To Accelerate the Gpcr Dru...mentioning

confidence: 99%

“…A protocol that combines reverse molecular docking to obtain interactions between key residues and 2D‐convolutional neural networks to extract interaction features has also been proposed to rapidly assess the neurotoxicity of molecules (McCarthy et al, 2022). The pipeline was showcased to identify toluene both as a GABA B receptor agonist and a toxin with potential neurological activity (McCarthy et al, 2022; Win‐Shwe & Fujimaki, 2010).…”

Section: At What Stages Can Ai Be Employed To Accelerate the Gpcr Dru...mentioning

confidence: 99%

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The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Nguyen

Koh

et al. 2023

British J Pharmacology

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The application of artificial intelligence (AI) approaches to drug discovery for G protein‐coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand‐GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process “faster, smarter and cheaper,” we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery.

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Computational investigation of nanoemulsion-released tocopherol for treating cardiovascular diseases: Insights from molecular dynamic simulation and deep learning prediction

Yakoubi

2024

Journal of Molecular Structure

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Reverse molecular docking and deep-learning to make predictions of receptor activity for neurotoxicology

Cited by 3 publications

References 53 publications

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Computational investigation of nanoemulsion-released tocopherol for treating cardiovascular diseases: Insights from molecular dynamic simulation and deep learning prediction

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