I’ve been reading “𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐟𝐨𝐫 𝐋𝐢𝐟𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 𝐚𝐧𝐝 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞” by Ivan Reznikov, published by O'Reilly, and here’s what stood out to me: In 𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲 𝐀𝐈, the way we represent molecules may shape how models “understand” chemistry. 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲-𝐭𝐮𝐧𝐞𝐝 𝐋𝐋𝐌𝐬 𝐝𝐨𝐧’𝐭 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐞𝐬 𝐥𝐢𝐤𝐞 𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐬 𝐝𝐨. They interpret them as 𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐬 𝐨𝐟 𝐭𝐨𝐤𝐞𝐧𝐬. Those tokens can come in different molecular representations: • 𝐒𝐌𝐈𝐋𝐄𝐒 • 𝐒𝐄𝐋𝐅𝐈𝐄𝐒 • 𝐈𝐧𝐂𝐡𝐈 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐞𝐫𝐬 This creates a fascinating challenge for generative AI: 👉 𝐖𝐡𝐢𝐜𝐡 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐠𝐢𝐯𝐞𝐬 𝐋𝐋𝐌𝐬 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐫𝐞𝐚𝐬𝐨𝐧 𝐚𝐛𝐨𝐮𝐭 𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲? 𝐒𝐌𝐈𝐋𝐄𝐒 is compact and widely used, but struggles with ambiguity, stereochemistry, and incomplete molecular context. 𝐒𝐄𝐋𝐅𝐈𝐄𝐒 is more robust because every generated sequence maps to a valid molecule.…