Artificial intelligence (AI) has exploded in popularity. It powers models that help us drive vehicles, proofread emails and even design new molecules for medications.
American Chemical SocietyAug 18 2024 Artificial intelligence has exploded in popularity. It powers models that help us drive vehicles, proofread emails and even design new molecules for medications. But just like a human, it's hard to read AI's mind. Explainable AI , a subset of the technology, could help us do just that by justifying a model's decisions.
As scientists, we like justification. If we can come up with models that help provide some insight into how AI makes its decisions, it could potentially make scientists more comfortable with these methodologies." One way to provide that justification is with XAI. These machine learning algorithms can help us see behind the scenes of AI decision making.
Related StoriesIn addition to identifying important molecular structures, the researchers hope to use XAI to improve predictive AI models. "XAI shows us what computer algorithms define as important for antibiotic activity," explains Sturm. "We can then use this information to train an AI model on what it's supposed to be looking for," Davis adds.
Antibiotic Antibiotic Resistance Drug Discovery Machine Learning Molecule Penicillin Q Research Technology
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