Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Abstract

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).

Uncovering Structure–Activity Relationships of Phenethylamines: Paving the Way for Innovative Mental Health Treatments

“…40 In such cases, QSAR studies gain heightened significance as they provide valuable insights, particularly when the structural information on the target protein is unavailable. 25 A statistically reliable NET model was successfully developed using a meticulously curated dataset of 52 compounds. The careful selection of this dataset ensures the statistical reliability of our model.…”

“…Elucidating the molecular basis of NET inhibition poses a challenge due to the absence of the 3D structure of the human NET . In such cases, QSAR studies gain heightened significance as they provide valuable insights, particularly when the structural information on the target protein is unavailable . A statistically reliable NET model was successfully developed using a meticulously curated dataset of 52 compounds.…”

“…By leveraging the power of in-silico methods, researchers can expedite the discovery and development of less toxic compounds, ultimately advancing mental health therapeutics. 24,25 In this study, we have used a QSAR-based approach to identify and characterize the potential of phenethylaminebased derivatives to act as MAT reuptake inhibitors. We have developed new and statistically reliable QSAR models for each MAT: SERT, DAT, and NET.…”

“…In particular, these computational techniques enable researchers to design and screen potential MAT inhibitors with improved safety profiles. By leveraging the power of in-silico methods, researchers can expedite the discovery and development of less toxic compounds, ultimately advancing mental health therapeutics. , …”