Technical Breakdown Of Key Architectures

Technical breakdown of key architectures, Let’s dive into the technical breakdown of key architectures commonly used in AI research:

1. Transformer Architecture (Core of Models like AlphaFold, BioGPT, ChemBERTa)

The Transformer model revolutionized deep learning by introducing self-attention mechanisms for improved sequence processing.

Key Components:

🔹 Input Embedding:

• Converts data (e.g., text, protein sequences) into dense vectors that represent meaningful features.

🔹 Positional Encoding:

• Since Transformers lack recurrence (like RNNs), they use sinusoidal functions to encode positional information.

🔹 Multi-Head Self-Attention (MHSA):

• Enables the model to focus on multiple aspects of the input sequence simultaneously.

• Each “head” processes information from different parts of the sequence.

Formula:

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax} \left( \frac{QK^T}{\sqrt{d_k}} \right) VAttention(Q,K,V)=softmax(dk​​QKT​)V

Where:

• Q = Query (what we’re focusing on)

• K = Key (reference points in the sequence)

• V = Value (the actual data points)

• dkd_kdk​ = Dimension of the key vector (scaling factor to stabilize gradients)

🔹 Feed-Forward Network (FFN):

• Fully connected layers that refine the attention outputs.

🔹 Residual Connections & Layer Normalization:

• Ensures gradient stability and faster convergence.

2. Graph Neural Networks (GNNs)

GNNs excel at processing data structured as graphs (e.g., molecules, protein interactions).

Key Concepts:

🔹 Node Features:

• Each node represents an entity (e.g., atoms in a molecule).

🔹 Edge Features:

• Define the relationships between nodes (e.g., chemical bonds).

🔹 Message Passing:

• Information propagates across connected nodes.

Formula for Node Update:

hi(l+1)=σ(W⋅∑j∈N(i)hj(l))h_i^{(l+1)} = \sigma \left( W \cdot \sum_{j \in N(i)} h_j^{(l)} \right)hi(l+1)​=σ​W⋅j∈N(i)∑​hj(l)​​

Where:

• hih_ihi​ = Node feature vector

• $N(i)$ = Neighbor nodes

• $W$ = Learnable weight matrix

• $\sigma$ = Activation function (e.g., ReLU)

3. Diffusion Models (Emerging in Drug Discovery & Materials Science)

Diffusion models generate data by reversing a noise-injection process.

Key Steps:

🔹 Forward Process:

• Gradually adds noise to a data sample until it becomes pure noise.

🔹 Reverse Process (Denoising):

• Uses a neural network to reconstruct the original data from the noisy version.

Mathematical Model:

pθ(x0∣xt)=N(x0;μθ(xt,t),Σθ(xt,t))p_{\theta}(x_0 | x_t) = \mathcal{N}(x_0; \mu_{\theta}(x_t, t), \Sigma_{\theta}(x_t, t))pθ​(x0​∣xt​)=N(x0​;μθ​(xt​,t),Σθ​(xt​,t))

Where:

• x0x_0x0​ = Original data

• xtx_txt​ = Noisy data at time step $t$

• μθ\mu_{\theta}μθ​ = Predicted mean by the model

• Σθ\Sigma_{\theta}Σθ​ = Predicted variance

4. U-Net (Used in MONAI for Medical Imaging)

U-Net is a convolutional neural network (CNN) designed for image segmentation.

Key Features:

🔹 Encoder-Decoder Structure:

• The encoder extracts features, while the decoder reconstructs the segmented output.

🔹 Skip Connections:

• Links between encoder and decoder layers retain detailed spatial information.

Choosing the Right Model

• For text-heavy research: Transformer models like BioGPT excel.

• For molecular and structural data: GNNs offer powerful insights.

• For image analysis in medicine or materials science: U-Net and MONAI are ideal.

• For generative data creation: Diffusion models are gaining traction.