meilerlab

AF2_GPCR_Kinase

A series of scripts that facilitate the prediction of user-defined protein structural properties using AlphaFold2

applications

bcl

The Biochemical Library (BCL) integrates traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. The cheminformatics toolkit contains customizable tools for the design, processing, and analysis of small molecules for computer-aided drug discovery.

discovery-self-peptides-hypertension

Official repository containing code, protocol capture, and files used to generate data for the manuscipt entitled "Discovery of post-translationally modified self-peptides that promote hypertension"

FlexPepDockNCAA

Official repository for the manuscript "Rosetta Flexpepdock to predict peptide-MHC binding: an approach for non-canonical amino acids"

gnn-descriptor

MolKGNN

MolKGNN is a deep learning model for predicting biological activity or molecular properties. It features in 1. SE(3)-invariance 2. conformation-invariance 3. interpretability. MolKGNN uses a novel molecular convolution to leverage the similarity of molecular neighborhood and kernels. It shows superior results in realistic drug discovery datasets.

PLM_restraint

probabilities_design

PTMPrediction

Supporting information for predicting and engineering post-translational modifcations using Rosetta

meilerlab/MolKGNN

MolKGNN is a deep learning model for predicting biological activity or molecular properties. It features in 1. SE(3)-invariance 2. conformation-invariance 3. interpretability. MolKGNN uses a novel molecular convolution to leverage the similarity of molecular neighborhood and kernels. It shows superior results in realistic drug discovery datasets.

meilerlab/AF2_GPCR_Kinase

A series of scripts that facilitate the prediction of user-defined protein structural properties using AlphaFold2

meilerlab/PLM_restraint

meilerlab/PTMPrediction

Supporting information for predicting and engineering post-translational modifcations using Rosetta

meilerlab/FlexPepDockNCAA

Official repository for the manuscript "Rosetta Flexpepdock to predict peptide-MHC binding: an approach for non-canonical amino acids"

meilerlab/applications

meilerlab/bcl

The Biochemical Library (BCL) integrates traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. The cheminformatics toolkit contains customizable tools for the design, processing, and analysis of small molecules for computer-aided drug discovery.

meilerlab/discovery-self-peptides-hypertension

Official repository containing code, protocol capture, and files used to generate data for the manuscipt entitled "Discovery of post-translationally modified self-peptides that promote hypertension"

meilerlab/gnn-descriptor

meilerlab/probabilities_design

meilerlab/bcl_cheminfo_review_2022

Input files for the BCL cheminformatics toolkit review

meilerlab/computational_models

Directory containing predicted computational structural models

meilerlab/KCNQ1-PyTorch

Implementation of the KCNQ1 project in PyTorch

meilerlab/HEPC3

meilerlab/Isolating-the-Impact-of-Post-Translational-Modification-on-MHC-Peptide-Binding-and-TCR-Engagement

Supplemental manuscript materials

meilerlab/ML_PLS_MHC_peptide_binding_pred

A machine learning application using PLS to predict binding affinity between MHC(A0201) and peptide containing NCAAs