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

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• The serotonergic system is complex, requiring advanced tools for drug development.
• Accurate prediction of molecular affinity to serotonin receptors and transporters is crucial.

Purpose of the Study:

• To introduce SerotoninAI, a novel web application for predicting molecule affinity to serotonergic targets.
• To provide researchers with an accessible tool for assessing drug-like properties.

Main Methods:

• SerotoninAI utilizes molecular structure (SMILES) to predict pKi values for serotonin receptors and transporters.
• The application assesses blood-brain barrier penetration and human intestinal absorption.
• Developed using Streamlit, it features an intuitive user interface.

Main Results:

• SerotoninAI offers predictions of pKi values for key serotonergic targets.
• It provides insights into essential drug attributes, aiding early-stage drug development.
• The tool is freely accessible without registration or fees.

Conclusions:

• SerotoninAI enhances drug development by providing rapid, accurate affinity predictions for serotonergic targets.
• The application democratizes access to essential drug discovery tools, supporting global research efforts.
• Its open-source nature facilitates further development and integration.