Marriage of Intuition and AI: Unlocking the Potential of Chemists and AI in Drug Discovery

Saturday - November 11 2023, 18:08 UTC - 1 year ago

Now, a new study in Nature Communications marries intuition and AI, producing a machine learning system that captures a chemist’s intuition for drug development. By analyzing feedback from 35 chemists, the team developed an AI model to find chemicals that are compatible with human biology. Protein prediction AI such as AlphaFold, RoseTTAFold, and their offshoots make it easier to model structures of proteins, however, finding the drug that fits it is a difficult matter, which is where the chemists come in. They have used software to sort through databases of chemicals looking for a match for the target proteins.

Intuition and AI make a strange couple. Intuition is hard to describe. It’s that gut feeling that gnaws at you, even if you don’t know why. We naturally build intuition through experience. Gut feelings aren’t always right; but they often creep into our subconscious to supplement logic and reasoning when making decisions. AI, in contrast, rapidly learns by digesting millions of cold, hard data points, producing purely analytical—if not always reasonable—results based on its input .

Now, a new study in Nature Communications marries the odd pair, resulting in a machine learning system that captures a chemist’s intuition for drug development. By analyzing feedback from 35 chemists at Novartis, a pharmaceutical company based in Switzerland, the team developed an AI model that learns from human expertise in a notoriously difficult stage of drug development: finding promising chemicals compatible with our biology .

First, the chemists used their intuition to choose which of 5,000 chemical pairs had a higher chance of becoming a useful drug. From this feedback, a simple artificial neural network learned their preferences. When challenged with new chemicals, the AI model gave each one a score that ranked whether it was worthy for further development as medication. Without any details on the chemical structures themselves, the AI "intuitively" scored certain structural components, which often occur in existing medications, higher than others .

Surprisingly, it also captured nebulous properties not explicitly programmed in previous computer modeling attempts. Paired with a generative AI model, like DALL-E, the robo-chemist designed a slew of new molecules as potential leads. Many promising drug candidates were based on "collative know-how," wrote the team. The study is a collaboration between Novartis and Microsoft Research AI4Science, the latter based in the UK .

Down the Chemical Rabbit Hole Most of our everyday medicines are made from small molecules—Tylenol for pain, metformin for diabetes management, antibiotics to fight off bacterial infections. But finding these molecules is a pain. First, scientists need to understand how the disease works. For example, they decipher the chain of biochemical reactions that give you a pounding headache. Then they find the weakest link in the chain, which is often a protein, and model its shape .

Structure in hand, they pinpoint nooks and crannies that molecules can jam into to disrupt the protein’s function, thereby putting a stop to the biological process—voilà, no more headaches. Thanks to protein prediction AI, such as AlphaFold, RoseTTAFold, and their offshoots, it’s now easier to model the structure of a target protein. Finding a molecule that fits it is another matter. The drug doesn’t just need to alter the target’s activity .

It also must be easily absorbed, spread to the target organ or tissue, and be safely metabolized and eliminated from the body. Here’s where medicinal chemists come in. These scientists are pioneers in the adoption of computer modeling. Over two decades ago, the field began using software to sift enormously large databases of chemicals looking for a compound to match a target protein.