hardMultiple Select
Generative AI Leader Practice Question: A research team wants to leverage Google…
A research team wants to leverage Google DeepMind's work to accelerate drug discovery. They are interested in using a model that predicts protein structures and another that can generate novel protein sequences with desired properties. Which TWO Google DeepMind achievements are most relevant? (Select 2 options.)
⚠ Common exam trap
Candidates often confuse Google DeepMind's general-purpose AI models (like Gemini or WaveNet) with domain-specific scientific models (like AlphaFold and AlphaProteo). They may select familiar names without verifying their specific application in drug discovery.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AlphaFold
AlphaFold (D) is correct because it is Google DeepMind's breakthrough model for predicting protein 3D structures from amino acid sequences, which directly accelerates drug discovery by enabling researchers to understand target proteins. AlphaProteo (E) is correct because it is DeepMind's AI system designed to generate novel protein sequences that bind to specific targets, effectively creating new proteins with desired therapeutic properties.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
WaveNet
Why it's wrong here
WaveNet generates raw audio waveforms for speech synthesis, offering no protein-structure prediction or sequence generation capability. It is tempting because DeepMind's generative modelling reputation spans domains, and WaveNet would be correct for text-to-speech or audio-generation tasks rather than drug discovery.
- ✗
Gemini
Why it's wrong here
Gemini is a general-purpose multimodal model family, not a protein-structure predictor or protein-sequence generator. It is tempting because Gemini's broad scientific reasoning appears applicable to biology, and it would be correct for literature synthesis or general research assistance rather than structural biology.
- ✗
AlphaCode
Why it's wrong here
AlphaCode generates competitive-programming source code, providing no protein folding or protein design functionality. It is tempting because AlphaCode's generative search over structured outputs resembles protein sequence design, and it would be correct for code-generation or algorithmic problem-solving tasks instead.
- ✓
AlphaFold
Why this is correct
AlphaFold directly satisfies the protein structure prediction requirement, using deep learning to determine 3D protein structures from amino acid sequences. Its accuracy at atomic-level prediction accelerates target identification in drug discovery, precisely matching the stem's demand for a model that predicts protein structures.
- ✓
AlphaProteo
Why this is correct
AlphaProteo designs novel proteins that bind specific targets, addressing the stem's need for a model generating sequences with desired properties. AlphaFold instead predicts existing structures, so AlphaProteo fills the generative half of the drug-discovery requirement.
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