Category Archives: Deep Learning

A Seq2Seq model for ETF forecasting

Owing to the misguided belief that I can achieve the impossible, I decided to build a model with the goal of beating the stock market.

Strap in, we’re about to get rich.

Machine learning is increasingly being employed by hedge funds to help mitigate risk and identify patterns and opportunities, whether this is for optimisation of algo trading strategies, fraud detection, high-frequency trading, or sentiment analysis. Arguably the most obvious, difficult, and naïve application of fintech ML is direct stock market forecasting – sounds like the perfect place to start.

Target

First things first, we need to decide on a stock to forecast. Volatility provides opportunities, but predictable volatility is even better. We need a security that swings in response to actual, reported events, and one whose trends roughly move somehow with other stocks – our hypothesis being that wider events in the market can be used to forecast a single security. SPDR GLD seems like a reasonable option – gold is such a popular hedge against global instability it’s price usually moves in the opposite direction to stocks such as DJIA or SP500 and moves with global disaster.

Gold price (/oz) in Pounds from 1980-2024

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Taking Equivariance in deep learning for a spin?

I recently went to Sheh Zaidi‘s brilliant introduction to Equivariance and Spherical Harmonics and I thought it would be useful to cement my understanding of it with a practical example. In this blog post I’m going to start with serotonin in two coordinate frames, and build a small equivariant neural network that featurises it.

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Understanding positional encoding in Transformers

Transformers are a very popular architecture in machine learning. While they were first introduced in natural language processing, they have been applied to many fields such as protein folding and design.
Transformers were first introduced in the excellent paper Attention is all you need by Vaswani et al. The paper describes the key elements, including multiheaded attention, and how they come together to create a sequence to sequence model for language translation. The key advance in Attention is all you need is the replacement of all recurrent layers with pure attention + fully connected blocks. Attention is very efficeint to compute and allows for fast comparisons over long distances within a sequence.
One issue, however, is that attention does not natively include a notion of position within a sequence. This means that all tokens could be scrambled and would produce the same result. To overcome this, one can explicitely add a positional encoding to each token. Ideally, such a positional encoding should reflect the relative distance between tokens when computing the query/key comparison such that closer tokens are attended to more than futher tokens. In Attention is all you need, Vaswani et al. propose the slightly mysterious sinusoidal positional encodings which are simply added to the token embeddings:

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Current strategies to predict structures of multiple protein conformational states

Since the release of AlphaFold2 (AF2), the problem of protein structure prediction is widely believed to be solved. Current structure prediction tools, such as AF2, are able to model most proteins with high accuracy. These methods, however, have a major limitation as they have been trained to predict a single structure for a given protein. Proteins are highly dynamic molecules, and their function often depends on transitions between several conformational states. Despite research focusing on the task of predicting the structures of multiple conformations of a protein, currently, no accurate and reliable method is available. In this blog post, I will provide a short overview of the strategies developed for predicting protein conformations. I have grouped these into three sets of related approaches. To conclude, I will also demonstrate how to run one of these strategies on your own.

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A simple criterion can conceal a multitude of chemical and structural sins

We’ve been investigating deep learning-based protein-ligand docking methods which often claim to be able to generate ligand binding modes within 2Å RMSD of the experimental one. We found, however, this simple criterion can conceal a multitude of chemical and structural sins…

X-ray crystal structure of ligand in PDB ID 1t9b.
Intertwined rings of the ligand from 1t9b.

DeepDock attempted to generate the ligand binding mode from PDB ID 1t9b
(light blue carbons, left), but gave pretzeled rings instead (white carbons, right).

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Lucubration or Gaslighting?​

Or: The best lies have a nugget of truth in them.​

Lucubration – The action or occupation of intensive study originally by candle or lamplight.

Gaslighting – Psychological abuse in which a person or group causes someone to question their own sanity, memories, or perception.

I was recently having a play with Google Bard. Bard, unlike ChatGPT has access to live data. It also undergoes live feedback and quality control. I was hoping to see if it would find me any journals with articles on prion research which I’d previously overlooked.

Me: Please show me some recent articles about prion research.
(Because always be polite to our AI overlords, they’ll remember!)

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What can you do with the OPIG Immunoinformatics Suite? v3.0

OPIG’s growing immunoinformatics team continues to develop and openly distribute a wide variety of databases and software packages for antibody/nanobody/T-cell receptor analysis. Below is a summary of all the latest updates (follows on from v1.0 and v2.0).

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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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Can AlphaFold predict protein-protein interfaces?

Since its release, AlphaFold has been the buzz of the computational biology community. It seems that every group in the protein science field is trying to apply the model in their respective areas of research. Already we are seeing numerous papers attempting to adapt the model to specific niche domains across a broad range of life sciences. In this blog post I summarise a recent paper’s use of the technology for predicting protein-protein interfaces.

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The Most ReLU-iable Activation Function?

The Rectified Linear Unit (ReLU) activation function was first used in 1975, but its use exploded when it was used by Nair & Hinton in their 2010 paper on Restricted Boltzmann Machines. ReLU and its derivative are fast to compute, and it has dominated deep neural networks for years. The main problem with the activation function is the so-called dead ReLU problem, where significant negative input to a neuron can cause its gradient to always be zero. To rectify this (har har), modified versions have proposed, including leaky ReLU, GeLU and SiLU, wherein the gradient for x < 0 is not always zero.

A 2020 paper by Naizat et al., which builds upon ideas set out in a 2014 Google Brain blog post seeks to explain why ReLU and its variants seem to be better in general for classification problems than sigmoidal functions such as tanh and sigmoid.

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