Bio/AI Software EngineerGlossary
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3D Tensor -
A three-dimensional array of numbers, often used in deep learning to represent structured data such as spatial grids, sequences, or stacked embeddings.Example:
AlphaFold uses 3D tensors to represent spatial relationships between amino acids.
A
AlphaFold -
A deep learning model developed by DeepMind that predicts 3D structures of proteins from amino acid sequences.Example:
AlphaFold was used to generate a predicted 3D structure for an unknown protein in the project.Amino Acid -
The basic building block of proteins. Each protein is a sequence of amino acids folded into a specific shape.Example:
The sequence of amino acids determines the structure of a protein.
B
Bio AI Software Engineer -
An engineer who builds intelligent software, tools, and infrastructure that apply machine learning to biological data, accelerating breakthroughs in protein design, drug discovery, and molecular simulation.Bioinformatician -
A scientist who uses computational tools to analyze and interpret biological data, often focusing on genomic sequences, protein structures, and biological networks.
C
Computational Biologist -
A Computational Biologist uses biological data to develop models to better understand biological systems. Conducts analysis using computational and mathematical methods and large data sets.
D
DNA -
A molecule that encodes genetic instructions used to make proteins and other cellular functions.Example:
The DNA sequence determines the order of amino acids in a protein.
E
Embedding -
A numerical vector representation of data, such as a protein or a molecule, that captures semantic or structural features in a lower-dimensional space.Example:
Each protein sequence was converted into a fixed-length embedding for similarity analysis.
M
Machine Learning Engineer -
A Machine Learning Engineer focuses on researching, building and designing self-running artificial intelligence systems to automate predictive models. ML engineers design and create AI algorithms capable of learning and making predictions that define machine learning.Example:
Building a model to predict protein-ligand binding affinities using historical data.Machine Learning -
An approach where algorithms learn from data to make predictions or classify unseen samples.Example:
A machine learning model was trained to predict protein-ligand binding strength.Model Inference -
The process of using a trained machine learning model to generate predictions or outputs based on new input data.Example:
During inference, the model predicted binding scores for unseen protein-ligand pairs.
N
Neural Network -
A type of machine learning model composed of layers of connected units (neurons) capable of learning complex patterns.Example:
A neural network was used to predict structural properties of the protein.
P
Protein -
A large molecule made up of amino acids, responsible for most biological functions in cells, including catalysis, signaling, and structural support.Example:
The protein kinase plays a role in signaling pathways.
T
Tokenization -
The process of splitting raw input data (such as a protein sequence) into smaller parts (tokens) that a model can process.Example:
The protein sequence was tokenized into individual amino acids before feeding into the transformer.