Cocalc Interactions Ipynb

This is a collection of interactions, mostly from the book. If you have are reading a print version of the book, or are reading it online via Github or nbviewer you will be unable to run the interactions. So I have created this notebook. Here is how you run an interaction if you do not have IPython installed on your computer. Go to try.juptyer.org in your browser. It will launch a temporary notebook server for you.

Click the New button and select Python 3. This will create a new notebook that will run Python 3 for you in your browser. Copy the entire contents of a cell from this notebook and paste it into a 'code' cell in the notebook on your browser. CoCalc: Collaborative Calculations and Data Science “A graphic is not ‘drawn’ once and for all; it is ‘constructed’ and reconstructed until it reveals all the relationships constituted by the interplay of the data. The best graphic operations are those carried out by the decision-maker themself.” — Jacques Bertin

Visualization provides a powerful means of making sense of data. A single image, however, typically provides answers to, at best, a handful of questions. Through interaction we can transform static images into tools for exploration: highlighting points of interest, zooming in to reveal finer-grained patterns, and linking across multiple views to reason about multi-dimensional relationships. At the core of interaction is the notion of a selection: a means of indicating to the computer which elements or regions we are interested in. For example, we might hover the mouse over a point, click multiple marks, or draw a bounding box around a region to highlight subsets of the data for further scrutiny. Alongside visual encodings and data transformations, Altair provides a selection abstraction for authoring interactions.

These selections encompass three aspects: Input event handling to select points or regions of interest, such as mouse hover, click, drag, scroll, and touch events. In this lecture we will begin to learn about creating dashboard-type GUI with iPython widgets! The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython's widgets. At the most basic level, interact auto-generates UI controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively.

To use interact, you need to define a function that you want to explore. Here is a function that prints its only argument x. When you pass this function as the first argument to interact along with an integer keyword argument (x=10), a slider is generated and bound to the function parameter. Note that the semicolon here just prevents an out cell from showing up. When you move the slider, the function is called, which prints the current value of x. Apply Pauling's electronegativity scale to predict bond polarity and classify molecular interactions.

Calculate dipole moments, analyze ΔEN values, and visualize polarity relationships across diverse compounds from H₂O to MgO. Interactive R programming demonstrates how electronegativity differences determine bond character in CoCalc. This notebook contains Chapter 2 from the main Advanced Chemical Bonding with R in CoCalc notebook. For the complete course, please refer to the main notebook: Advanced Chemical Bonding with R in CoCalc.ipynb Linus Pauling's electronegativity scale (1932) quantifies an atom's ability to attract electrons in a chemical bond. The scale runs from 0.7 (Francium) to 4.0 (Fluorine).

The dipole moment (μ) quantifies molecular polarity: μ = δ × d Interact.jl provides interactive widgets for IJulia. Interaction relies on Observables.jl reactive programming package. Observables provides the type Observable which represent time-varying values. For example, a Slider widget can be turned into a "observable of numbers". Execute the following two cells, and then move the slider.

You will see that the value of observe(s) changes accordingly. Let us now inspect the types of these entities. You can have many instances of the same widget in a notebook, and they stay in sync: A slider is useless if you cannot do more with it than just watch its value. Thankfully we can transform one signal into another, which means we can transform the signal of values that the slider takes into, say a signal of it's squares: Go ahead and vary the slider to see this in action.

Explore structure-property relationships in drug discovery using computational analysis methods in CoCalc. Investigate molecular weight vs drug-likeness correlations, analyze lipophilicity-polarity relationships, and examine drug evolution trends over time. Learn to identify key molecular features affecting pharmaceutical properties, interpret QSAR patterns, and understand how structural modifications impact drug characteristics. This tutorial demonstrates advanced relationship analysis techniques essential for medicinal chemistry optimization, lead compound development, and rational drug design in pharmaceutical research applications. This notebook contains Part 7 from the main Molecular Descriptor Analysis for Drug Discovery in CoCalc notebook. For the complete course, please refer to the main notebook: Molecular Descriptor Analysis for Drug Discovery in CoCalc.ipynb

Drug discovery is a complex process that typically takes 10-15 years and costs over $1 billion per approved drug. Computational methods can significantly accelerate this process by: Virtual Screening: Evaluating millions of compounds computationally Perform comprehensive pharmaceutical database analysis using computational chemistry methods in CoCalc. Analyze molecular properties across entire drug libraries, evaluate compliance rates for drug-likeness criteria, and identify top-performing compounds using statistical methods. Learn to process multiple pharmaceutical compounds simultaneously, calculate aggregate molecular statistics, and perform systematic drug discovery analysis.

This tutorial demonstrates professional workflows for large-scale molecular property analysis and pharmaceutical compound evaluation used in medicinal chemistry research and virtual screening applications. This notebook contains Part 4 from the main Molecular Descriptor Analysis for Drug Discovery in CoCalc notebook. For the complete course, please refer to the main notebook: Molecular Descriptor Analysis for Drug Discovery in CoCalc.ipynb Drug discovery is a complex process that typically takes 10-15 years and costs over $1 billion per approved drug. Computational methods can significantly accelerate this process by: Virtual Screening: Evaluating millions of compounds computationally

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