biolexis Goto Github PK
Name: Biolexis Therapeutics Inc.
Type: Organization
Location: United States of America
Name: Biolexis Therapeutics Inc.
Type: Organization
Location: United States of America
a tool for protein-ligand binding affinity prediction
aaProperties takes in one or two multiple sequence alignments as input, βcleansβ the data into a suitable format and then converts it to a sequence of properties. Depending on if the user used one or two alignments as input, he or she can now either compare the two amino acid sequences against each other, compare the two property sequences against each other, compare individual amino acid sequences against their respective properties or write one of the pairs (amino acid plus properties) into a comma separated file.
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
the simple alchemistry library
Example π Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using π§ Amazon SageMaker.
(Linux) AMDock: Assisted molecular docking with AutoDock4 and AutoDockVina
Uses protein file codes to download the corresponding mmCif file from PDB and plot the number of each amino acid in the file
AutoDock for GPUs and other accelerators
AutoDock Vina
Python3 translation of AutoDockTools
The Binding Affinity Tool (BAT.py) is a fully automated tool for absolute binding free energy calculations on protein-ligand systems.
Python / C++ GUI application for fitting binding site data
De novo design of small molecule binding sites into proteins
Bio-YODIE is GATE's biomedical named entity linking pipeline.
BionoiNet is a deep learning-based software to classify ligand-binding sites.
Spatiotemporal identification of druggable binding sites using deep learning
Applications of nonequilibrium candidate Monte Carlo (NCMC) to ligand binding mode sampling
contains files for blues-flexible-ligand paper
Detect catalytic triads in serine protease PDB files based on geometry
Python classes / functions to parse Clinical Trial data in the form of XML returned from clinicaltrials.gov. Older version also allows for parsing of Json returned from Lilly COI API.
Multiple sequence aligner for protein and DNA/RNA.
Making Protein folding accessible to all via Google Colab!
Integrated physics-based and ligand-based modeling.
A D4M module for Julia. D4M was developed in MATLAB by Dr Jeremy Kepner.
A D4M module for Python. D4M was developed in MATLAB by Dr Jeremy Kepner
Molecular Manipulation Made Easy.
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Data-Driven Documents codes.
China tencent open source team.