{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yang K"],"funding":["Fundamental Research Funds for the Central Universities","Natural Science Foundation of Zhejiang Province","Zhejiang Health Information Association Research Program","Ministry of Science and Technology of the People's Republic of China","Scientific Research Fund of Zhejiang Provincial Education Department"],"pagination":["2941-2947"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11040716"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["64(8)"],"pubmed_abstract":["Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimi"],"journal":["Journal of chemical information and modeling"],"pubmed_title":["MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery."],"pmcid":["PMC11040716"],"funding_grant_id":["Y202352312","LD22H300004","G2022016010L","2023XHSZ-Z05","2022ZFJH003"],"pubmed_authors":["Jiang J","Yang K","Xie Z","Chen C","Wang J","Mei Q","Ju B","Qian X","Qu S","Sun N","Li Z","He T","Xu X","Xu Z"],"additional_accession":[]},"is_claimable":false,"name":"MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.","description":"Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimi","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2026-06-02T01:44:57.14Z","creation":"2025-04-06T03:17:20.422Z"},"accession":"S-EPMC11040716","cross_references":{"pubmed":["38563534"],"doi":["10.1021/acs.jcim.3c01979"]}}