[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120149-en":3,"doc-seo-120149-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120149,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Modeling PROTAC Degradation Activity with Machine Learning","PROTACs are a therapeutic modality that exploits the cell’s degradation machinery to selectively eliminate target proteins, but developing new PROTACs is resource-intensive and demands substantial domain expertise. This work proposes a strategy to curate open-source PROTAC data and an open-source deep learning tool to predict degradation activity for novel PROTAC molecules. The dataset integrates key factors including concentrations at half-maximal effect, E3 ligase type, POI amino acid sequence, and experimental cell type, using embedding-based architectures for protein and cell representation. Three tailored studies evaluate dataset quality and model generalization, reporting up to 80.8% top test accuracy and 0.865 ROC AUC, and 62.3% accuracy with 0.604 ROC AUC on novel protein targets.","Modeling PROTAC Degradation Activity with Machine Learning  \nStefano Ribesa , Eva Nittingerb , Christian Tyrchanb and Rocío Mercadoa,∗  \na Department of Computer Science and Engineering, Section for Data Science and AI, Chalmers University of Technology, Chalmersplatsen  \n4, Gothenburg, 412 96, Sweden  \nb Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, Mölndal, 431 83, Sweden  \narXiv :2406 .02637v2 [ q-bio .QM] 26 Sep 2024  \nARTICLE INFO  \nKeywords:  \nPROTAC Machine Learning Drug Discovery Protein Degradation  \nAB STRACT  \nPROTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as 􀁰􀁄􀁃50 , 􀁄􀁭􀁡􀁸 , E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequencesand cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 ROC AUC, and a test accuracy of 62.3% and 0.604 ROC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches.  \n1. Introduction  \nMachine learning (ML) has transformed various scientific domains, including drug design and discovery, by offering novel solutions to complex, multi-objective optimization challenges (Atance et al., 2022; Fromer and Coley, 2023; Gao et al., 2022; Winter et al., 2019) . In the context of medicinal chemistry, ML techniques have revolutionized the process of identifying and optimizing potential drug candidates. Traditionally, drug discovery has relied heavily on trial-and-error experimentation, which is not only timeconsuming but also expensive. ML techniques have the potential to significantly accelerate and improve this process by predicting properties of molecules in silico, such as binding affinity, solubility, and toxicity, with remarkable accuracy (Born et al., 2023; Gorantla et al., 2024; Vassileiou et al., 2023). This in turn saves time and money in early-stage drug discovery by focusing resources on the most promising candidates. At the same time, AI models’ high performance can potentially lead to better designed drugs for patients.  \nIn order to develop ML models for chemistry, ML algorithms leverage vast datasets containing molecular structures, biological activities, and chemical properties to learn intricate patterns and relationships, also called quantitative structureactivity relationships (QSAR) . These algorithms can discern subtle correlations and structure in molecular data that are  \n∗Corresponding author  \n [ribes@chalmers.se](ribes@chalmers.se) (S. Ribes); [eva.nittinger@astrazeneca.com](eva.nittinger@astrazeneca.com) (E. Nittinger); [christian.tyrchan@astrazeneca.com](christian.tyrchan@astrazeneca.com) (C. Tyrchan); [rocio.mercado@chalmers.se](rocio.mercado@chalmers.se) (R. Mercado)  \nORCID(s): 0009-0009-2774-8792 (S. Ribes); 0000-0001-7231-7996 (E. Nittinger); 0000-000","cbCais1IELOZLBG3","https://ap.wps.com/l/cbCais1IELOZLBG3","pdf",1745736,1,13,"English","en",105,"# Introduction\n## PROTACs and targeted protein degradation\n## Machine learning for drug design\n## Data curation and model approach","[{\"question\":\"Why are PROTACs promising yet difficult to develop?\",\"answer\":\"PROTACs can harness the cell’s degradation machinery for selective protein elimination, but creating new PROTACs requires significant expertise, time, and cost.\"},{\"question\":\"What does the proposed work contribute to PROTAC research?\",\"answer\":\"It introduces a strategy for curating open-source PROTAC data and an open-source deep learning tool that predicts degradation activity of novel PROTAC molecules.\"},{\"question\":\"Which inputs does the model use to predict PROTAC degradation activity?\",\"answer\":\"The curated dataset includes key information such as PROTAC concentration at half maximum, E3 ligase type, POI amino acid sequence, and experimental cell type.\"}]","Modeling PROTAC Degradation Activity with Machine Learning | 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are PROTACs promising yet difficult to develop?","Question",{"text":75,"@type":76},"PROTACs can harness the cell’s degradation machinery for selective protein elimination, but creating new PROTACs requires significant expertise, time, and cost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed work contribute to PROTAC research?",{"text":80,"@type":76},"It introduces a strategy for curating open-source PROTAC data and an open-source deep learning tool that predicts degradation activity of novel PROTAC molecules.",{"name":82,"@type":73,"acceptedAnswer":83},"Which inputs does the model use to predict PROTAC degradation activity?",{"text":84,"@type":76},"The curated dataset includes key information such as PROTAC concentration at half maximum, E3 ligase type, POI amino acid sequence, and experimental cell 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