[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-dataset-shows-where-chemistry-ai-models-fall-short":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},7338,"new-dataset-shows-where-chemistry-ai-models-fall-short","New Dataset Shows Where Chemistry AI Models Fall Short","A new 181,000-example training set shows even GPT-5.2 and Gemini-3-Flash struggle to predict how small chemical tweaks change a molecule's behavior.","A new benchmark shows that today's top AI models are surprisingly bad at predicting how a single substituent swap changes a molecule's properties.\n\nResearchers built MolSC, a dataset of 181,000 examples pulled from manually annotated bioactivity records, each capturing how attaching a specific substituent to a molecular scaffold shifts properties like structural-alert risk, target-specific bioactivity, and physicochemical traits. They paired it with MolSC-Bench, a 1,541-example holdout test built so the scaffolds, substituents, and molecules never overlap with the training data. When they ran existing molecular LLMs and general-purpose models, including GPT-5.2 and Gemini-3-Flash, against the benchmark, all of them struggled to reliably predict how these small structural tweaks change a molecule's behavior. Fine-tuning a model on MolSC closed that gap and also boosted performance on other, unrelated molecular tasks.\n\nSubstituent swaps are exactly the kind of edit medicinal chemists make every day while optimizing a drug candidate, so a model that can't reason about them isn't ready to assist with real design work. It's also a data point against the assumption that scaling general-purpose LLMs automatically buys domain expertise: two current flagship models, GPT-5.2 and Gemini-3-Flash, still lost to a model trained on a narrow, well-curated dataset.\n\nMore parameters didn't buy these models chemistry intuition. Targeted data did.","[\"ai\",\"chemistry\",\"machine-learning\",\"benchmarks\"]","2026-09-23T04:00:00.000Z","2026-09-23T08:13:39.497Z","2026-09-23T08:13:45.448Z","published",null,[],"ai",[24,26,27,28],"chemistry","machine-learning","benchmarks",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.23073",0,{"sections":35},[36,39,43,48,53,57,61,66,71,76,81,86,91,96],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",4297,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",710,{"name":44,"slug":45,"count":46,"latest_published_at":47},"Policy","policy",369,"2026-09-23T02:13:52.000Z",{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",202,"2026-09-22T23:00:04.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":18},"Hardware","hardware",169,{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",133,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",110,"2026-09-22T20:00:00.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",80,"2026-09-22T23:32:52.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Dev Tools","dev-tools",79,"2026-09-22T22:21:13.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Startups","startups",65,"2026-09-22T22:06:48.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",45,"2026-09-22T15:35:06.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"General","general",43,"2026-09-21T23:48:56.000Z",{"name":92,"slug":93,"count":94,"latest_published_at":95},"Reviews","reviews",27,"2026-09-22T13:00:00.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]