Software for performing Automatic Ligand-guided Backbone Ensemble Receptor Optimization with ICM

A toolkit for predicting the binding mode of small molecules interacting with proteins based on interfacial rigidification, as assessed by graph theoretic constraint counting on the covalent and noncovalent bond network. Raschka et al. (2016) Proteins: Structure, Function, and Bioinformatics

[Master Thesis 2017] Scripts for calculating metrics to assess performance of a drug design software.

A Framework for Virtual Screnning

screenlamp is a Python toolkit for hypothesis-driven virtual screening

Course schedule from experience as a selected Undergraduate Teaching Assistant at the University of Pittsburgh course, BIOSC 1540 - Computational Structural Biology, taught by Dr. Jacob Durrant.

Utilities for analyzing and reporting results from Smina virtual screens

Virtual screening on PriA-SSB and RMI-FANCM with the LifeChem library

Maximal Unbiased Benchmarking Datasets for Histone Deacetylases and Sirtuin Family

Maximal Unbiased Benchmarking Datasets for human Chemokine Receptors Family

Datasets used in the tox21 challenge

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

Analysis of COVID19 Candidates using molecular descriptors from mol2 files

Automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KNIME software

MIPT 2019 Bachelor's project "Pipeline for search of off-target ligand connections"

A software for analysis and fast virtual screen of KEGG_DRUG molecular database of accepted drugs classificated by ATC codes

V2DB (Virtual 2D Materials Database): the code for generating and predicting the novel 2D materials by virtual screening.

A Bash script to submit batch jobs to ORCA, OpenBabel, MGLTools, and Autodock Vina. Automatically checks for syntax errors in ORCA input files, and can also identify ORCA runtime crashes. Organizes log files into individual directories, and creates an easy to read results summary text file. Created by Rac Mukkamala

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Prediction model used in the paper: Accelerated Design of Near-Infrared-II Molecular Fluorophores via First-Principle Understanding and Machine Learning.