Python package for graph neural networks in chemistry and biology
- deep-learning
- graph-neural-networks
- dgl
- cheminformatics
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Python package for graph neural networks in chemistry and biology
In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more
A Go package for engineering organisms.
Interactive network visualization in Python and Dash, powered by Cytoscape.js
The Chemistry Development Kit
Lightweight, super fast C/C++ (& Python) library for sequence alignment using edit (Levenshtein) distance.
Molecular Processing Made Easy.
Vision-Language Pathology Foundation Model - Nature Medicine
Dicoogle - Open Source PACS
Graph-linked unified embedding for single-cell multi-omics data integration
A Practical and Efficient NCBI Taxonomy Toolkit, also supports creating NCBI-style taxdump files for custom taxonomies like GTDB/ICTV
A dependency-free cross-platform swiss army knife for PDB files.
Universal cheminformatics toolkit, utilities and database search tools
Deep Reinforcement Learning for de-novo Drug Design
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Graph-based molecule modeling toolkit for cheminformatics
Molecule Validation and Standardization
AI-based pathology predicts origins for cancers of unknown primary - Nature
Open source tumor evidence matching for sequencing labs and hospitals without molecular tumor boards. Research prototype; oncologist review required.
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
Data visualisation web components for the life sciences.
A tool for cell instance aware segmentation in densely packed 3D volumetric images