Advances with deep learning, the growth of databases of molecules for virtual screening and improvements in computational power have supported the emergence of a new field of quantitative structure–...

The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its high data dependency, Machine Learning QSAR models will be directly influenced by the activit...

Artificial feed-forward neural networks have long been recognized as powerful machine learning models and are widely used in QSAR and QSPR modeling of molecular properties. Inspired by Random Forest m...

Storks, overfitting and a descriptor glut

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.

Publication date: Available online 26 September 2026 Source: Emerging Contaminants Author(s): Ling Ma, Zihe Kong, Xiaofeng Peng, Ruixin Tian, Adejoke Adewumi Agbailu, Wenlai Xu, Shuxia Xu, Min Zhong, Ziqiang Yin

Previous << || >> Next   In an earlier post I considered what it might mean to describe drug design as AI-based. In this post I’ll take a ...

YouTube video by Stefan Vergu

Reliable quantification of uncertainty is critical for the interpretation and regulatory use of the QSAR models. Applicability domain (AD) assessment was introduced precisely for this purpose─the orig...