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Steven Shave's Projects

aminonaut icon aminonaut

Software to identify and analyse nullomer peptides from UniProt data

keepn icon keepn

KeepN is a C++ templated header only library to keep the best or worst N of something.

kinetic-multisite-binding icon kinetic-multisite-binding

Analytical solutions to 1:many binding events are only derivable up to 1:3. A kinetic treatment removes this limitation

mandelbrot_gmp icon mandelbrot_gmp

Toy arbitrary precision Mandelbrot generator and double precision Buddhabrot generator

mmpdb icon mmpdb

A package to identify matched molecular pairs and use them to predict property changes.

mumol icon mumol

Format for highly compressed small molecules. Highly efficient encoding for SDF files.

pbsec icon pbsec

Python code for running simple plate-based size exclusion (pbSEC) simulations

pulse icon pulse

Phage Library Sequence Evaluation, a tool to analyse protein sequence coverage of phage display libraries

pyautogui icon pyautogui

A cross-platform GUI automation Python module for human beings. Used to programmatically control the mouse & keyboard.

pybindingcurve icon pybindingcurve

Binding curve simulation and experimental data fitting for multi component protein-ligand systems

rdkit icon rdkit

The official sources for the RDKit library

rdkonf icon rdkonf

High quality small molecule conformer generator using RDKit and the method outlined by Ebejer et. al. J.Chem.Inf.Model 2012

similaritylab icon similaritylab

SimilarityLab; a website for running molecular similarity and target prediction.

smi2svg icon smi2svg

Useful site to help visualise molecules and their properties

ssknn icon ssknn

Header only C++ K-Nearest Neighbours

stockpredictionai icon stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

usrcat icon usrcat

USRCAT - Ultrafast Shape Recognition with Credo Atom Types

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