Development of Diphenethylamines as Selective Kappa Opioid Receptor Ligands and Their Pharmacological Activities

Schmidhammer, Helmut; Erli, Filippo; Guerrieri, Elena; Spetea, Mariana

doi:10.3390/molecules25215092

Cited by 11 publications

(7 citation statements)

References 97 publications

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“…The Kappa Opioid Receptor is one of four opioid receptors that belong to the G-protein-coupled receptors (GPCR) superfamily. Research on opioid receptors, particularly on KOR, has been gaining momentum as it mediates affective disorders such as depression and anxiety, neurological diseases like epilepsy, but also pain and drug addiction, making it a promising pharmacological target [ 37 – 40 ]. The ADORA2A is also a part of the GPCR superfamily and mediates pain, motor control, and mood [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Designing optimized drug candidates with Generative Adversarial Network

et al. 2022

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Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder–Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder–Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder–Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model’s ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine $$A_ {2a}$$ A 2 a receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

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Section: Methodsmentioning

confidence: 99%

Designing optimized drug candidates with Generative Adversarial Network

et al. 2022

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“…The Kappa Opioid Receptor is one of four opioid receptors that belong to the G-protein-coupled receptors (GPCR) superfamily. Research on opioid receptors, particularly on KOR, has been gaining momentum as it mediates affective disorders such as depression and anxiety, neurological diseases like epilepsy, but also pain and drug addiction, making it a promising pharmacological target [27,28,29,30]. The ADORA2A is also a part of the GPCR superfamily and mediates pain, motor control, and mood [31].…”

Section: Case-studymentioning

confidence: 99%

Designing Optimized Drug Candidates With Generative Adversarial Network

Abbasi

Santos

Pereira

et al. 2022

Preprint

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Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted property. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine A2a Receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

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“…Therefore, when treating patients with cancer, the dosage and treatment duration of opioids should be weighed ( 186 , 190 ). To design opioid analgesics without side effects, it is important to understand the molecular mechanisms, signaling pathways and effects of KOR in combination with chemotherapeutic drugs ( 191 ). For example, by decreasing entry of opioids into the central nervous system, selective targeting of KOR to the peripheral nervous system and inflammatory tissues is biased towards activation of analgesia-associated intracellular signaling pathways.…”

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Opioids in cancer: The κ‑opioid receptor (Review)

et al. 2021

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The κ-opioid receptor (KOR) is one of the primary receptors of opioids and serves a vital role in the regulation of pain, anesthesia, addiction and other pathological and physiological processes. KOR is associated with several types of cancer and may influence cancer progression. It has been proposed that KOR may represent a new tumor molecular marker and provide a novel basis for molecular targeted therapies for cancer. However, the association between KOR and cancer remains to be explored comprehensively. The present review introduces KOR and its association with different types of cancer. Improved understanding of KOR may facilitate development of novel antitumor therapies. Contents1. Introduction 2. Discovery, structure and typing of KOR 3. Expression and physiological function of KOR 4. Expression, function and significance of KOR in various types of cancer 5. Potential roles of KOR in cancer 6. Effects of opioids on tumors 7. Conclusion and future prospects

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Development of Diphenethylamines as Selective Kappa Opioid Receptor Ligands and Their Pharmacological Activities

Cited by 11 publications

References 97 publications

Designing optimized drug candidates with Generative Adversarial Network

Designing optimized drug candidates with Generative Adversarial Network

Designing Optimized Drug Candidates With Generative Adversarial Network

Opioids in cancer: The κ‑opioid receptor (Review)

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