Educational materials for, and related to, UC Irvine's Drug Discovery Computing Techniques course (PharmSci 175/275), currently taught by David Mobley.

RxDock is a fork of rDock. Note: the latest code is under development. Please do git checkout patched-rdock after clone if you want patched rDock. [IMPORTANT NOTE: pull requests should be posted on GitLab, this is a read-only source code mirror]

screenlamp is a Python toolkit for hypothesis-driven virtual screening

Flexible Artificial Intelligence Docking

🔷 MX pipeline. Refinement and ligand screening.

Code for an artificial neural net classifier of small molecule GPCR activity

Your one-stop solution for protein-ligand docking. This pipeline simplifies molecular docking, helping researchers study protein-ligand interactions efficiently. It offers clear instructions and customizable options for easy virtual screening. Simplify drug discovery, explore confidently!

This pipeline facilitates setting up ligand docking against a protein using AutoDock-GPU. It streamlines the process of docking a ligand library onto a protein structure, leveraging the enhanced performance of AutoDock-GPU for faster results.

Computational Drug Screening Platform

Web service for scoring protein-ligand complexes

To identify the lowest (relatively) energy docking sites for a particular ligand using one-dimensional search.

Flexible Artificial Intelligence Docking

A quick and dirty virtual screening task for potential ligands of Flavobacterium johnsoniae Tyrosine Ammonia Lyase (FjTAL)

Developed as part of the Lawrence Livermore National Laboratory Data Science Summer Institute 2022 Challenge Problem. Screening molecular inhibitors for SARS-CoV-2 protein targets with Deep Learning Models.

Project in the Durrant Lab at UPitt that wanted to re-use code from a previous neural network ligand-protein interaction software to extract features for ML

Python program to run several PELE simulations in a very authomaticall way