The process of drug discovery is notoriously costly and time-consuming, with protein crystal identification being a critical step that traditionally requires substantial computational resources and expertise. The existing state-of-the-art model, MARCO, while effective, misses almost 10% of crystals and demands extensive computational effort. This inefficiency leads to missed opportunities in identifying potential drug candidates, directly impacting the speed and cost of bringing new medicines to market.
Appsilon introduced Crystal Clear Vision (CCV), an innovative machine learning model that leverages transfer learning and Low-Rank Adaptation (LoRA) for fine-tuning, significantly outperforming MARCO.
By focusing on enhancing crystal detection accuracy and applying state-of-the-art techniques, CCV dramatically lowers the missed crystal rate to 3.6% (reduction in lost crystals of 61%). Thanks to this boost in efficiency, larger scale surveys are enabled, leading to a reduced time to discovering new drug candidates.
Patrick Charbonneau
Duke University, Lead Researcher in MARCO initiative
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