Ispirova, G. | bioRxiv Science

bioRxiv2024

NetMedPy: A Python package for Large-Scale Network Medicine Screening

SummaryNetwork medicine leverages the quantification of information flow within sub-cellular networks to elucidate disease etiology and comorbidity, as well as to predict drug efficacy and identify potential therapeutic targets. However, current Network Medicine toolsets often lack computationally efficient data processing pipelines that support diverse scoring functions, network distance metrics, and null models. These limitations hamper their application in large-scale molecular screening, hypothesis testing, and ensemble modeling. To address these challenges, we introduce NetMedPy, a highly efficient and versatile computational package designed for comprehensive Network Medicine analyses. AvailabilityNetMedPy is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from https://github.com/menicgiulia/NetMedPy and https://pypi.org/project/NetMedPy. The package can run on any standard desktop computer or computing cluster.

bioinformatics↗

bioRxiv2024

CPIExtract: A software package to collect and harmonize small molecule and protein interactions

SummaryThe binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interactions (CPIs) are not harmonized into a single entity but rather scattered across multiple institutions, each maintaining databases with different formats. Extracting information from these multiple sources remains challenging due to data heterogeneity. Here, we present CPIExtract (Compound-Protein Interaction Extract), a Python package that automatically retrieves CPI data from nine major repositories, filters non-human and low-quality records, harmonizes chemical and protein identifiers, and computes unified pChEMBL binding values. Compared with MINER, a state-of-the-art CPI extraction algorithm, CPIExtract retrieves 85.5% more compounds, 16-fold more experimentally supported interactions, and over four times more proteins, substantially increasing the availability of strong and weak binders. The resulting harmonized dataset enables custom filtering and export in standard tabular formats for downstream applications such as network medicine, drug repurposing, and training of deep learning models. AvailabilityCPIExtract is an open-source Python package under an MIT license. CPIExtract can be downloaded from https://github.com/menicgiulia/CPIExtract and https://pypi.org/project/cpiextract. The package can run on any standard desktop computer or computing cluster.

bioinformatics↗