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Why Teaching AI to Learn From Almost Nothing Could Change Drug Discovery By Anoop Kumar Maurya | July 2026 | Series: AI for Drug Discovery — Article 1 of 10 Image: Finding the right drug is like finding the one key that opens a lock — from a box of a million candidates Imagine you are handed a completely new lock. No manual. No key. Just the lock — and a box of a million keys to try. Your job is to find the one that fits before the patient runs out of time. ...

Introduction to Reinforcement Learning for Absolute Beginners Image: A child learning to ride a bicycle through trial and error - the essence of reinforcement learning Imagine teaching a child to ride a bicycle. They learn by trying, wobbling, and maybe falling – trial and error guided by little victories and tumbles. Over time, they adjust their balance and steering to maximize the thrill of coasting (and minimize the painful falls). This process of trial-and-error learning is exactly what reinforcement learning (RL) is all about. In RL, a computer agent learns from feedback: it takes actions, observes the outcomes (rewards or penalties), and adapts its behavior to get better results in the future. Just as you might avoid actions that make a puppy grumpy and repeat those that make it wag its tail, an RL agent learns to favor actions that lead to positive rewards. ...