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As machine learning and deep learning technologies advance thanks to advances in computation, algorithms and data availability, the possibilities of the technology continue to expand in medicine. While these AI-driven approaches have real potential, such systems demand large volumes of representative data, careful privacy and security scrutiny and thoughtful long-term strategic planning. In this Q&A, Kathryn Rough, associate director of the Center for Advanced Evidence Generation at IQVIA, discusses the impact of deep learning on healthcare delivery and recommends steps to take during the design, training, evaluation and deployment phases to increase the likelihood that these models will be safe, effective and ethical when trained on real-world health data. Rough also explores the role of epidemiologists in evaluating these technologies as part of multidisciplinary teams and provides advice for health…Contrastive learning-based model ConPLEx elevates drug-protein interaction predictions
[Generative AI image from Tahsin/Adobe Stock]
Drug discovery, traditionally a labor-intensive process, often involves extensive computational work during experimental screening. Advances in AI, however, promise to streamline this process. To that end, a team from MIT and Tufts has introduced ConPLex, a computational model that uses large language model techniques, similar to those behind ChatGPT. The model analyzes vast amounts of text data to discern patterns and relationships among amino acids. The technique matches potential drug molecules to their target proteins without requiring complex molecular structure computation. The system’s efficiency allows it to sift through an array of more than 100 million compounds in a single day.Bonnie Berger, head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and one of the senior authors of the new study, ex…