SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery

Abstract

SerotoninAI is an innovative web application for scientific purposes focused on the serotonergic system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value) of a molecule to all main serotonin receptors and serotonin transporters based on molecule structure introduced as SMILES. Additionally, the application provides essential insights into critical attributes of potential drugs such as blood−brain barrier penetration and human intestinal absorption. The complexity of the serotonergic system demands advanced tools for accurate predictions, which is a fundamental requirement in drug development. SerotoninAI addresses this need by providing an intuitive user interface that generates predictions of pKi values for the main serotonergic targets. The application is freely available on the Internet at https://serotoninai.streamlit.app/, implemented in Streamlit with all major web browsers supported. Currently, to the best of our knowledge, there is no tool that allows users to access affinity predictions for serotonergic targets without registration or financial obligations. SerotoninAI significantly increases the scope of drug development activities worldwide. The source code of the application is available at https://github.com/nczub/SerotoninAI_streamlit.

Integrated QSAR Models for Prediction of Serotonergic Activity: Machine Learning Unveiling Activity and Selectivity Patterns of Molecular Descriptors

“…Models of serotonergic activity, as well as the selectivity model, have become new extensions of SerotoninAI, a new web application related to serotonergic QSAR models [ 30 ] described in the article [ 31 ]. Applicability domain information was implemented in a form unified with other SerotoninAI modules.…”

“…Considerations related to the sulfur atom in relation to the pIC50 value for the 5-HT6 receptor appear in a study by Bukhari S.N.A. et al Based on the QSAR model created, followed by a docking step to confirm the results obtained, among other things, a sulfur Models of serotonergic activity, as well as the selectivity model, have become new extensions of SerotoninAI, a new web application related to serotonergic QSAR models [30] described in the article [31]. Applicability domain information was implemented in a form unified with other SerotoninAI modules.…”

Editorial: Machine Learning in Bio-cheminformatics

“…Some methods, algorithms, and functional tools were constructed to facilitate the application or improve the performance of the classic virtual screening strategy. − Machine learning methods were also adopted in this collection to identify new hit compounds, discover promising leads for cholestasis, interpret QSAR models, and learn molecular representations. − DiStefano et al and Mao et al conducted research on toxicity prediction and antiviral drug design, respectively. Moreover, ML was also applied to explore the pharmaceutical properties of diverse drug candidates. − A novel knowledge base for nonalcoholic fatty liver disease was developed . Heyndrickx et al adopted cross-pharma federated learning to unleash the benefit of QSAR.…”