Streamlining Your Terminal Commands With Custom Bash Functions and Aliases

If you’ve ever found yourself typing out the same long commands over and over again, or if you’ve ever wished you could teleport directly to your favourite directories, then this post is for you.

Before we jump into some useful examples, let’s go over what bash functions and aliases are, and how to set them up.

Bash Functions vs Aliases

A bash function is like a mini script stored in your .bashrc or .bash_profile file. It can accept arguments, execute a series of commands, and even return a value.

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Unclear documentation? ChatGPT can help!

The PyMOL Python API is a useful resource for most people doing research in OPIG, whether focussed on antibodies, small molecule drug design or protein folding. However, the documentation is poorly structured and difficult to interpret without first having understood the structure of the module. In particular, the differences between use of the PyMOL command line and the API can be unclear, leading to a much longer debugging process for code than you’d like.

While I’m reluctant to continue the recent theme of ChatGPT-related posts, this is a use for ChatGPT that would have been incredibly useful to me when I was first getting to grips with the PyMOL API.

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Coding a Progress Bar for your Google Slides Presentation

Presentations are a great opportunity to explain your work to a new audience and receive valuable feedback. A vital aspect of a presentation is keeping the audience’s attention which is generally quite tricky I have found (from experience).

One thing that I have noticed other presenters using, which has helped maintain my focus, is an indication of the progression of the presentation. Including in your slides information that there are only a few slides remaining, encourages the listeners to keep their focus for a little longer.

Instead I will show you how to do it using Apps Script, Google’s cloud platform that allows you to write JavaScript code which can work with its online products such as Docs or Slides.

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Writing a BLOPIG Post With ChatGPT: A Personal Take on Using AI for Assisted Writing

Disclaimer: I used ChatGPT to improve the writing style of this article, in combination with some personal curation before obtaining a final version.

You’ve probably heard it all already, from ChatGPT writing code and doing proofreading for you to a rap battle between OPIG’s Antibodies and Small Molecules groups, and more.

Whether you like it or not, ChaGPT has unleashed people’s creative side regarding applications and attempts to find shortcuts. Questionable? Absolutely!

In this BLOPIG post, I show how I used ChatGPT to easily write a post summarising some material of my own intellectual property, which I presented as part of my group meeting talk. Mainly, I list some personal thoughts on the ethical concerns around using ChatGPT to assist your writing.

To start off, I passed on content from my own publication draft to ChatGPT, asking to generate a blog post in plain English for BLOPIG. The outcome:

Not bad.

But, it made me realise a number of things:

  • With great power comes great responsibility [Uncle Ben – Spiderman].
    You are responsible for the ethics that go into using ChatGPT. Are you faking expertise? Are you being actually lazy or just being efficient? Think twice (or many more times) if you’re doing the right thing.
  • It can significantly reduce the number of writing iterations but don’t take it at face value.
    Can you actually trust the plain output? No.
    Never take its output as the ground truth, as Large Language Models such as ChatGPT often produce biased writing outputs.
    Keep in mind that whatever you produce as a scientist will be picked up by others, and prone to drive misinformation, if incorrect. It is OK to reduce mechanical iterations, but it’s NOT OK to skip quality control.
  • Be open about it.
    You don’t want to set the wrong example for your colleagues. So, mention if you use it, how you used it, and it is fine to encourage efficiency, but not incentivising a culture of scientific misconduct and plagiarism. Don’t skip the step of producing quality ideas on your own. This is such a concern that publishers like Elsevier have already reacted by publishing guidelines contemplating this possibility. While Nature Springer is working on ways to spot AI-generated outputs.

The bottom line

What are the dos and don’ts of using ChatGPT?

Yes, use it to have fun. Yes, use it to proofread or polish your writing. Yes, use it to summarise your own ideas. No, don’t use it to do the analysis and interpretation of your results. No, don’t copy and paste its direct output into your publication. No, don’t hide that you used it. Finally, NO, you can’t add ChatGPT as a contributing author!

Train Your Own Protein Language Model In Just a Few Lines of Code

Language models have token the world by storm recently and, given the already explored analogies between protein primary sequence and text, there’s been a lot of interest in applying these models to protein sequences. Interest is not only coming from academia and the pharmaceutical industry, but also some very unlikely suspects such as ByteDance – yes the same ByteDance of TikTok fame. So if you also fancy trying your hand at building a protein language model then read on, it’s surprisingly easy.

Training your own protein language model from scratch is made remarkably easy by the HuggingFace Transformers library, which allows you to specify a model architecture, tokenise your training data, and train a model in only a few lines of code. Under the hood, the Transformers library uses PyTorch (or optionally Tensorflow) models, allowing you to dig deeper into customising training or model architecture, or simply leave it to the highly abstracted Transformers library to handle it all for you.

For this article, I’ll assume you already understand how language models work, and are now looking to implement one yourself, trained from scratch.

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The State of Computational Protein Design

Last month, I had the privilege to attend the Keystone Symposium on Computational Design and Modeling of Biomolecules in beautiful Banff, Canada. This conference gave an incredible insight into the current state of the protein design field, as we are on the precipice of advances catalyzed by deep learning.

Here are my key takeaways from the conference:

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How ChatGPT changed my writing as an ESL speaker

It’s not always easy to live in an Anglophone scientific world when English isn’t your first language. When careers are built upon the ability to communicate ideas clearly and eloquently, struggling to find the right words can be a real hindrance to explain your science in a way that is taken seriously. Contrary to popular belief, it’s not something you can simply “work” on. Often, it doesn’t matter how many books you’ve read, how many years of education you have, or how articulate you are in your original language — your brain will refuse to summon the right expression, or get stuck in a construction that a native speaker would never use. Struggling with a second language is very much a biological phenomenon.

The standard recommendation for ESL (English as a Second Language) speakers has long been to ask a native colleague to read through any text that needs to be published or submitted somewhere (such as an article or a grant application). Well-intentioned as this advice may be, there are multiple problems with it. Lingua franca or not, only 15% of the world population speaks English, of which only 5% are native speakers — meaning that for most scientists not working in Anglophone countries, the option is rarely available. Even when available, it is unreasonable to expect these colleagues to add charitable proof-reading to their workload simply because they happened to be born speaking a different language. But, most importantly, I have always felt — and I want to emphasize that I truly believe most people who issue this kind of advice to be well-intentioned — that the underliying message sounds too much like “you need vetting by a member of our select linguistic club if you want your ideas to be taken seriously“.

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Be a computational chemist and you must be a jack of all trades

Being a jack of all trades brings to mind someone who has extensive multidisciplinary expertise and is equipped with many tools in their toolbox to solve different problems. A jack of all trades is a great succinct description for computational chemists in drug discovery.

Recently I had a great conversation with Dr. Arjun Narayanan, a Senior Research Scientist at Vertex Pharmaceuticals and a jack of all trades as a computational chemist. In this blog post, I’ll describe what he does as a computational chemist, the problems he solves, and the new tools he’s looking forward to adding to his toolbox.

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