AI in Drug Discovery

Question 1

What recent trend has AI experienced in drug discovery, according to the lecture?

Answer 1

AI is booming in biology and chemistry, with a rapid increase in AI-designed drugs entering clinical trials, significantly impacting drug discovery processes.

Question 2

What are the primary benefits of using AI in drug development?

Answer 2

Benefits include reduced animal testing, earlier toxicity prediction, faster synthesis, fewer compounds to screen, and reduced overall costs in drug development.

Question 3

Does AI replace traditional drug discovery elements?

Answer 3

No, AI accelerates traditional processes like molecular screening, structural predictions (e.g., AlphaFold), and predictive modelling, allowing for faster outcomes without replacing core methods.

Question 4

What is the difference between AI and Machine Learning (ML)?

Answer 4

AI broadly refers to techniques that enable machines to mimic human behavior, while ML is a subset of AI focused on algorithms that learn from data and improve over time.

Question 5

What are the three main types of machine learning, and what characterizes each?

Answer 5

Unsupervised Learning: Finds patterns in unlabeled data (e.g., clustering).
Supervised Learning: Learns from labeled data to make predictions (e.g., classification, regression).
Reinforcement Learning: Improves by trial and error with feedback to optimize predictions.