Introduction

This project aims to build a model that would aid novel drug design processes and is hosted here: https://github.com/susanzhang233/mollykill_2.0. Somewhat related to this mollykill 1.0, this project hopes to simplify limitations of the generator and decoder by disregarding GAN’s over-complicated generative structure. In this 2.0 version, we’ll be more focused on employing the accuracy and efficiency of the discriminator. Then, instead of letting the generator to come up with new molecules starting from zero. We’ll be applying a larger real world molecule datasets(ie. Zinc15), to mimic the traditional virtual/actual screening process to come up with potential inhibitors.

Introduction

This project is aimed to build a Graphic GAN model that would aid drug design processes. The project is also hosted on github in the following link: https://github.com/susanzhang233/mollykill. For demonstration, the project is expected to learn graphical features of the molecules that are experimentally tested with inhibition effect for the specific protein SARS coronavirus 3C-like Protease (3CLPro) . Then, the model would develop a reasonable way to generate potential novel molecules inhibitors’ graphically representations. After that, with a defeaturizer, the graphical representations would be converted into visualizable molecule formats.

Overview

My project, Mollykill, aims to comprehensively apply the informations regarding PIC16B and beyond in crafting a basic generative molecular model and providing a basic idea for computational methodologies in the process of drug design.

In this blog, we’ll build a machine learning model with Tensorflow that helps us to classify fake news.

In this blog post, I’ll write a tutorial on a simple version of the spectral clustering algorithm for clustering data points.

In this blog, we’ll demonstrate the basic utilization of the plotly pacakge, and some basic database usage with sql(Structured Query Language). Databases increases the efficiency for treating large datasets. We will be using the National Oceanic and Atmospheric Admistration’s (NOAA) climate data set, together with temperature stations and country codes, to create some interactive geographically representative plots corresponding to temperatures.

In this blog post assignment (homework), I create a short post for my new website. The primary purpose is to practice working with Jekyll blogging with Python code.

Text text text

In this blog post, you’ll create several interesting, interactive data graphics using the NOAA climate data that we’ve explored in the first several weeks of lectures.

In this blog post assignment (homework), you’ll create a short post for your new website. The primary purpose is to give you some practice working with Jekyll blogging with Python code.

In this post, we’ll get set up with Jekyll. Jekyll is a static site converter, which you can use to turn plaintext documents into attractive webpages.

Fortunately, it’s pretty easy to embed interactive HTML figures produced via Plotly on your blog. Just use plotly.io.write_html() to save your figure. Then, copy the resulting HTML file to the _includes directory of your blog. Finally, place the code

The purpose of this homework assignment is to get you set up with the software tools we’ll use in PIC16B, including Anaconda, git + GitHub, and Jekyll.

It is possible to construct, maintain, and update your blog fully from GitHub. In this case, it is not necessary to download your blog’s files or modify them on your computer. However, when constructing complex posts involving code and figures, local editing can be more comfortable. Additionally, since GitHub Pages usually takes a few minutes to publish all your changes, modifying your blog locally allows you to more quickly see the results of your changes, including errors when they arise. In this post, I’ll show how to manage your blog locally.

In this post, I’ll show how to create a helpful histogram of some synthetic data.

In this post, we’ll see some examples of how to create technical posts that include Python code, explanatory text, and notes about your learnings. We’ll go over the primary methods that you’ll use to embed content in your posts.