ESM3 is all-to-all generative model that both conditions on and generates a variety of different tracks. As input, ESM3 is conditioned on various tracks as described in Appendix A.1.5.1, and as output, ESM3 generates predictions detailed in Appendix A.1.5.2.
The generative pipeline is as follows.
Tokenization First, raw inputs are tokenized as described in Appendix A.1.3. Structural inputs are tokenized via a VQ-VAE (Appendix A.1.7). Function keywords are tokenized by quantizing the TF-IDF transform of functional keywords with locality sensitive hashing (LSH), detailed in Appendix A.1.8.
Transformer Trunk A standard Transformer $(57,58)$ architecture processes the post-tokenized inputs. Geometric Attention (Algorithm 6 and Fig. S2) directly processes structural coordinates as input. Model outputs are logits over token space, and can be sampled to obtain outputs described in Appendix A.1.5.2. The overall architecture is diagrammed in Fig. S1.
Decoder Most tracks can be naively decoded into tokens detailed in Appendix A.1.3. Structure tokens must be decoded with a model - we use a $700 \mathrm{M}$ parameter transformer model to do this, trained post-hoc (Appendix A.1.7.2). The decoder uses sequence tokens and structure tokens to directly predict coordinates, pTM, and pLDDT (59). Function tokens are decoded using a small 3-layer transformer, trained post-hoc to invert the LSH quantization procedure (Appendix A.1.8.2.1).
Please explain this in detail to a beginning grad student in machine learning.
Response:
Introduction
ESM3 is a state-of-the-art generative model designed to handle and generate various types of data representations, often referred to as "tracks". In machine learning, particularly in natural language processing and computational biology, generative models like ESM3 are powerful tools that can learn complex data distributions and generate new data samples that are coherent and meaningful.
As a beginning grad student in machine learning, you might be familiar with concepts like tokenization, transformer architectures, and generative models. In this explanation, we'll delve into the details of ESM3's architecture and its generative pipeline, breaking down each component to help you understand how the model operates.
Overview of ESM3
- All-to-All Generative Model: ESM3 is designed to both condition on (take as input) and generate multiple types of data, or "tracks". These tracks can include sequences, structures, and functional annotations among others.
- Purpose: By handling multiple data modalities, ESM3 aims to capture the complex relationships between different aspects of data (e.g., protein sequences and their 3D structures) and generate coherent outputs across these modalities.
Generative Pipeline of ESM3
The generative pipeline of ESM3 consists of three main stages:
- Tokenization
- Transformer Trunk
- Decoder
Let's explore each stage in detail.
1. Tokenization
What is Tokenization?
- In the context of machine learning models, tokenization is the process of converting raw data into a numerical format that can be processed by the model.
- Tokens are discrete units that represent elements of the input data, such as words in a sentence or amino acids in a protein sequence.
ESM3's Tokenization Process
- Raw Inputs: ESM3 takes raw data as input, which may include sequences, structural information, and functional annotations.
- Tracks: Each type of data (sequence, structure, function, etc.) is referred to as a "track".
Tokenization Steps
- Sequence Tokenization:
- Sequences (e.g., protein sequences) are tokenized using standard methods where each amino acid or nucleotide is assigned a unique token.
- This is similar to how words are tokenized in natural language processing.
- Structural Inputs Tokenization via VQ-VAE:
- Structural Data: Refers to 3D coordinates of molecules or other spatial information.
- Vector Quantized Variational Autoencoder (VQ-VAE):
- VQ-VAE is a type of autoencoder that compresses continuous data into a discrete latent space.
- Purpose: To represent complex structural data as discrete tokens that can be processed by the transformer model.
- Process:
- The structural data is passed through an encoder to obtain latent representations.
- These representations are quantized to the nearest codebook vector, resulting in discrete tokens.
- The decoder can then reconstruct the original structural data from these tokens.
- Function Keywords Tokenization via TF-IDF and LSH:
- Functional Keywords: Descriptive words or phrases that annotate the function of the data (e.g., protein function annotations).
- TF-IDF Transformation:
- Term Frequency-Inverse Document Frequency (TF-IDF) is a numerical statistic that reflects how important a word is to a document in a collection.
- It is commonly used in information retrieval and text mining.
- Purpose: To convert textual functional annotations into numerical vectors reflecting their importance.
- Locality Sensitive Hashing (LSH):
- LSH is a method for hashing input items so that similar items map to the same "buckets" with high probability.
- Purpose: To quantize the TF-IDF vectors into discrete tokens efficiently, preserving similarity.
- Process:
- The TF-IDF vectors are hashed using LSH to produce discrete tokens.
- These tokens represent the functional annotations in a form suitable for the transformer model.
What is a Transformer?
- The Transformer architecture is a type of neural network model introduced in the paper "Attention is All You Need" (Vaswani et al., 2017).
- It relies on a mechanism called self-attention to weigh the significance of different parts of the input data.
- Transformers have become the foundation for many state-of-the-art models in NLP and beyond (e.g., GPT models, BERT).
ESM3's Transformer Trunk
- Purpose: The core of ESM3 is a transformer model that processes the tokenized inputs from all tracks.
- Processing Tokenized Inputs:
- The tokens from sequences, structures, and functions are concatenated or otherwise combined to form a single input sequence.
- This allows the model to learn relationships across different data modalities.
Geometric Attention
- Problem with Structural Data: Structural coordinates are continuous numerical data and may not fit neatly into the discrete token paradigm.
- Geometric Attention:
- An extension of the standard attention mechanism to directly handle geometric data (e.g., spatial coordinates).
- Algorithm 6 and Fig. S2 (from the original paper) likely detail how geometric attention is implemented.
- Purpose: To allow the model to directly process and attend to structural coordinates within the transformer framework.
Model Outputs
- Logits over Token Space:
- After processing the inputs, the transformer outputs logits, which are raw scores for each possible token in the output space.
- Sampling:
- These logits can be used to generate tokens by applying a softmax function and sampling.
- This is how the model generates new sequences or predictions.
3. Decoder
Purpose of the Decoder
- After obtaining the output tokens from the transformer, these tokens need to be converted back into meaningful data representations (e.g., sequences, structures).
Decoding Processes
- Naive Decoding for Most Tracks:
- For tracks where the tokens directly correspond to the original data (like sequences), decoding is straightforward.
- Process:
- Map each token back to its corresponding element (e.g., amino acid).
- Reconstruct the sequence by concatenating these elements.
- Decoding Structure Tokens with a Separate Model:
- Challenge: Structure tokens derived from VQ-VAE quantization represent complex spatial data and cannot be directly mapped back.
- Solution: Use a separate transformer model to decode these tokens.
- 700M Parameter Transformer:
- A large transformer model trained separately (post-hoc) to reconstruct the structural coordinates from the structure tokens.
- Training:
- This model is trained on pairs of structure tokens and the corresponding original coordinates.
- Outputs:
- Directly predicts the 3D coordinates.
- May also output confidence measures like pTM and pLDDT scores (used to assess the quality of protein structure predictions).
- Decoding Function Tokens with a Small Transformer
- Challenge: Function tokens quantized via LSH need to be mapped back to the original functional annotations.
- Solution: Use a small, 3-layer transformer trained to invert the LSH quantization.
- Training:
- This decoder model is trained on pairs of LSH-quantized tokens and the original TF-IDF vectors or annotations.
- Purpose:
- Effectively inverts the quantization process to retrieve the original or approximate functional annotations.
Putting It All Together
End-to-End Generation:
The model can generate new data across multiple tracks by sampling from the output logits and decoding the tokens appropriately.
This allows for generating sequences with corresponding structural and functional information.
Conditioning on Input Tracks:
ESM3 can take various tracks as input to condition its generation.
For example, given a sequence, it can generate the corresponding structure and function annotations.
Applications:
Such a model is valuable in fields like bioinformatics, where understanding the relationships between sequences, structures, and functions of proteins is crucial.
Can be used for tasks like protein design, where one might specify a desired function or structure and generate a corresponding sequence.
Key Concepts Explained
VQ-VAE (Vector Quantized Variational Autoencoder):
Combines variational autoencoders (VAEs) with discrete latent representations.
Useful for compressing continuous data into discrete tokens.
TF-IDF (Term Frequency-Inverse Document Frequency):
A numerical statistic that reflects how important a word is to a document in a corpus.
High TF-IDF score means the word is more unique to the document.
LSH (Locality Sensitive Hashing):
A technique for dimensionality reduction that preserves similarity.
Maps similar input vectors to the same hash bucket with high probability.
Self-Attention and Transformers:
Self-attention allows the model to weigh the importance of different parts of the input when making predictions.
Transformers use multi-head attention mechanisms to capture different types of relationships.
pTM and pLDDT Scores:
pTM (Predicted Template Modeling Score):
- Reflects the overall predicted accuracy of the protein structure, focusing on the global fold.
pLDDT (Predicted Local Distance Difference Test Score):
- Provides per-residue confidence scores for the predicted structure.
- Higher scores indicate greater confidence in the accuracy of the predicted local structure.
Conclusion
ESM3 represents an advanced integration of multiple machine learning techniques to handle complex, multimodal data. By combining tokenization strategies like VQ-VAE and TF-IDF with LSH, and leveraging transformers and geometric attention, ESM3 can model and generate sequences, structures, and functional annotations coherently.
Understanding ESM3 involves appreciating how different components work together:
- Tokenization transforms raw data into a form suitable for processing.
- The Transformer Trunk learns representations and relationships across different data types.
- Decoders reconstruct meaningful outputs from the model's predictions.
As a grad student, exploring each of these components in more depth will provide valuable insights into modern machine learning architectures and their applications in complex domains like computational biology.
References for Further Reading
Transformers and Attention Mechanisms:
Vaswani et al., 2017. Attention is All You Need.
A comprehensive introduction to the transformer architecture.
Variational Autoencoders and VQ-VAE:
Kingma & Welling, 2013. Auto-Encoding Variational Bayes.
Oord et al., 2017. Neural Discrete Representation Learning.
Locality Sensitive Hashing:
Indyk & Motwani, 1998. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality.
Protein Structure Prediction Metrics:
Jumper et al., 2021. Highly accurate protein structure prediction with AlphaFold.
Explanation of pTM and pLDDT scores.
Final Note
Understanding models like ESM3 requires integrating knowledge from various subfields of machine learning. Don't hesitate to delve deeper into each component and consider implementing simplified versions to solidify your understanding. Practical experience, combined with theoretical knowledge, will greatly enhance your grasp of these complex systems.