PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery

The advent of powerful machine learning algorithms as
well as the
availability of high volume of pharmacological data has given new
fuel to QSAR, opening new unprecedented options for deriving highly
predictive models for assisting the rationale design of new bioactive
compounds, for screening and prioritizing large molecular libraries,
and for repurposing new drugs toward new clinical uses. Here, we present
PoseidonQ (an acronym for Personal Optimization Software for Efficient
Implementation and Derivation of Online QSAR), a user-friendly software
solution designed to simplify the derivation of the QSAR model for
drug design and discovery. PoseidonQ incorporates 22 machine learning
algorithms, 17 types of molecular fingerprints, and 208 RDKit molecular
descriptors and enables the quick derivation of both regression and
classification models along with a calculated and easily interpretable
applicability domain. Importantly, the platform is automatically linked
to the latest version of the ChEMBL database, thus providing streamlined
access to large amounts of curated bioactivity data. Importantly,
the user is also given the option of gathering high-quality experimental
data based on customizable filtering settings. Noteworthy, PoseidonQ
facilitates the deployment of trained QSAR models as web-based applications
through seamless integration with Streamlit Cloud and GitHub, empowering
users to share, refine, and integrate models effortlessly. Interestingly,
the translation of QSAR models into web-based applications makes them
free accessible, portable, and ready for screening large volumes of
new data without limits. By unifying data preparation, model generation,
and deployment into an intuitive workflow, PoseidonQ makes advanced
QSAR modeling for drug design and discovery accessible to a wide audience
of researchers irrespective of their skill levels. PoseidonQ bridges
the gap between complex machine learning techniques and practical
drug discovery applications, enhancing the efficiency, collaboration,
and adoption of QSAR approaches in modern drug discovery programs.
PoseidonQ is available for Windows and Linux (ubuntu 22.04 distro)
operating systems and can be downloaded for free at https://github.com/Muzatheking12/PoseidonQ.