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

Overview

Journal J Chem Inf Model

Publisher American Chemical Society

Specialties Chemistry
Medical Informatics

Date 2024 Jan 30

PMID 38289046

Authors

Natalia Lapinska

Adam Paclawski

Jakub Szlek

Aleksander Mendyk

Affiliations

Soon will be listed here.

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.

Citing Articles

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Lapinska N, Szlek J, Paclawski A, Mendyk A Molecules. 2025; 30(3).

PMID: 39942741 PMC: 11819831. DOI: 10.3390/molecules30030637.

StreamChol: a web-based application for predicting cholestasis.

Rodriguez-Belenguer P, Soria-Olivas E, Pastor M J Cheminform. 2025; 17(1):9.

PMID: 39838478 PMC: 11752685. DOI: 10.1186/s13321-024-00943-9.

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

Lapinska N, Paclawski A, Szlek J, Mendyk A Pharmaceutics. 2024; 16(3).

PMID: 38543243 PMC: 10974160. DOI: 10.3390/pharmaceutics16030349.

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