{"doi":"10.1021/acs.jcim.2c00127","title":"Predicting Protein–Ligand Docking Structure with Graph Neural Network","abstract":"Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.","journal":"Journal of Chemical Information and Modeling","year":2022,"id":236314,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":70,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9499,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":245814,"name":"Jian Wang","orcid":"0000-0001-7768-2802","position":1,"is_corresponding":false},{"id":856228,"name":"Weilin Cong","orcid":"0000-0002-8726-3238","position":2,"is_corresponding":false},{"id":856229,"name":"Yihe Huang","orcid":"0000-0002-9642-1170","position":3,"is_corresponding":false},{"id":856230,"name":"Morteza Ramezani","orcid":"0000-0002-7498-5522","position":4,"is_corresponding":false},{"id":856231,"name":"Anup Sarma","orcid":"0000-0002-0098-4498","position":5,"is_corresponding":false},{"id":149320,"name":"Nikolay V. Dokholyan","orcid":"0000-0002-8225-4025","position":6,"is_corresponding":false},{"id":690917,"name":"Mehrdad Mahdavi","orcid":"0000-0003-2679-6679","position":7,"is_corresponding":false},{"id":690919,"name":"Mahmut Kandemir","orcid":"0000-0002-9940-9951","position":8,"is_corresponding":false},{"id":690915,"name":"Huaipan Jiang","orcid":"0000-0002-8160-1611","position":0,"is_corresponding":true}],"reference_count":64,"raw_metadata":null,"created_at":"2026-07-19T00:21:58.728756Z","pmid":"35699430","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}