Monthly Archives: October 2024

Navigating Hallucinations in Large Language Models: A Simple Guide

AI is moving fast, and large language models (LLMs) are at the centre of it all, doing everything from generating coherent, human-like text to tackling complex coding challenges. And this is just scratching the surface—LLMs are popping up everywhere, and their list of talents keeps growing by the day.

However, these models aren’t infallible. One of their most intriguing and concerning quirks is the phenomenon known as “hallucination” – instances where the AI confidently produces information that is fabricated or factually incorrect. As we increasingly rely on AI-powered systems in our daily lives, understanding what hallucinations are is crucial. This post briefly explores LLM hallucinations, exploring what they are, why they occur, and how we can navigate them and get the most out of our new favourite tools.

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Protein Property Prediction Using Graph Neural Networks

Proteins are fundamental biological molecules whose structure and interactions underpin a wide array of biological functions. To better understand and predict protein properties, scientists leverage graph neural networks (GNNs), which are particularly well-suited for modeling the complex relationships between protein structure and sequence. This post will explore how GNNs provide a natural representation of proteins, the incorporation of protein language models (PLLMs) like ESM, and the use of techniques like residual layers to improve training efficiency.

Why Graph Neural Networks are Ideal for Representing Proteins

Graph Neural Networks (GNNs) have emerged as a promising framework to fuse primary and secondary structure representation of proteins. GNNs are uniquely suited to represent proteins by modeling atoms or residues as nodes and their spatial connections as edges. Moreover, GNNs operate hierarchically, propagating information through the graph in multiple layers and learning representations of the protein at different levels of granularity. In the context of protein property prediction, this hierarchical learning can reveal important structural motifs, local interactions, and global patterns that contribute to biochemical properties.

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Testing python (or any!) command line applications

Through our work in OPIG, many of our projects come in the form of code bases written in Python. These can be many different things like databases, machine learning models, and other software tools. Often, the user interface for these tools is developed as both a web app and a command line application. Here, I will discuss one of my favourite tools for testing command-line applications: prysk!

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The XChem trove of protein–small-molecules structures not in the PDB

The XChem facility at Diamond Light Source is truly impressive feat of automation in fragment-based drug discovery, where visitors comes clutching a styrofoam ice box teeming with apo-form protein crystals, which the shifter soaks with compounds from one or more fragment libraries and a robot at the i04-1 beamline kindly processes each of the thousands of crystal-laden pins, while the visitor enjoys the excellent food in the Diamond canteen (R22). I would especially recommend the jambalaya. Following data collection, the magic of data processing happens: the PanDDA method is used to find partial occupancy in the density, which is processed semi-automatedly and most open targets are uploaded in the Fragalysis web app allowing the ligand binding to be studied and further compounds elaborated. This collection of targets bound to hundreds of small molecules is a true treasure trove of data as many have yet to be deposited in the PDB, making it a perfect test set for algorithm design: fragments are notorious fickle to model and deep learning models cannot cheat by remembering these from the protein database.

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Why the vegans will say “I told you so…”

I am writing this on Wednesday 2nd October 2024. The news has all eyes on the middle eastern skies. Yesterday a story was circulating on BBC news warning of a drop in uptake of the seasonal flu jab.

https://www.bbc.co.uk/news/articles/c62d8r0nnl6o

Four days ago, on Friday 27th September, several news outlets reported that several healthcare workers had shown flu-like symptoms following exposure to the first patient known to have contracted avian flu (H5N1) without any animal contact. PCR testing has been inconclusive, with none of these workers testing positive for signs of the virus.

https://www.bbc.co.uk/news/articles/czd1v3vn6ero

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Aider and Cheap, Free, and Local LLMs

Aider and the Future of Coding: Open-Source, Affordable, and Local LLMs

The landscape of AI coding is rapidly evolving, with tools like Cursor gaining popularity for multi-file editing and copilot for AI-assisted autocomplete. However, these solutions are both closed-source and require a subscription.

This blog post will explore Aider, an open-source AI coding tool that offers flexibility, cost-effectiveness, and impressive performance, especially when paired with affordable, free, and local LLMs like DeepSeek, Google Gemini, and Ollama.

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MDAnalysis: Work with dynamics trajectories of proteins

For a long time crystallographers and subsequently the authors of AlphaFold2 had you believe that proteins are a static group of atoms written to a .pdb file. Turns out this was a HOAX. If you don’t want to miss out on the latest trend of working with dynamic structural ensembles of proteins this blog post is exactly right for you. MDAnalysis is a python package which as the name says was designed to analyse molecular dyanmics simulation and lets you work with trajectories of protein structures easily.

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Our future health: A new UK health research programme

Last week I walked into Boots  and, after giving some physical measurements, including my blood pressure and cholesterol levels, I gave a blood sample to be part of the Our Future Health initiative. Our Future Health (https://ourfuturehealth.org.uk/)  is set to become the UK’s largest health research programme ever. With the aim of recruiting five million volunteers across the country, it aims to revolutionise the way we detect, prevent and treat disease.

The breadth, depth and detail of Our Future Health makes it a world-leading resource. The data collected could hold the key to a wide range of health discoveries, such as:

  • Identifying early signals to detect disease much earlier.
  • Accurately predicting who is at higher risk of disease.
  • Developing better interventions and more effective treatments and technologies.

How’s it going so far?

Since the start of recruitment in July 2022 (delyed because of Covid), the programme has recruited over one million participants where:

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Walk through a cell

In 2022, Maritan et al. released the first ever macromolecular model of an entire cell. The cell in question is a bacterial cell from the genus Mycoplasma. If you’re a biologist, you likely know Mycoplasma as a common cell culture contaminant.

Now, through the work of app developer Timothy Davison, you can interactively explore this cell model from the comfort of your iPhone or Apple Vision Pro. Here are three reasons why I like CellWalk:

1. It’s pretty

The visuals of CellWalk are striking. The app offers a rich depiction of the cell, allowing the user to zoom from the whole cell to individual atoms. I spent a while clicking through each protein I could see to see if I could guess what it was or what it did. Zooming out, CellWalk offers a beautiful tripartite cross section of the cell, showing first the lipid membrane, then a colourful jumble-bag of all its cellular proteins, and then finally the spaghetti-like polynucleic acids.

Tripartite cross section of a Mycoplasma cell. Screengrab taken from the CellWalk app on my phone.
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